Abstract
Nutraceutical interventions supporting microbiota and eliciting clinical improvements in metabolic diseases have grown significantly. Chronic stress, gut dysbiosis, and metainflammation have emerged as key factors intertwined with sleep disorders, consequently exacerbating the decline in quality of life. This study aimed to assess the effects of two nutraceutical formulations containing prebiotics (fructooligosaccharides (FOS), galactooligosaccharides (GOS), yeast β-glucans), minerals (Mg, Se, Zn), and the herbal medicine Silybum marianum L. Gaertn., Asteraceae (Milk thistle or Silymarin). These formulations, namely NSupple (without silymarin) and NSupple_Silybum (with silymarin) were tested over 180 days in overweight/obese volunteers from Brazil's southeastern region. We accessed fecal gut microbiota by partial 16S rRNA sequences; cytokines expression by CBA; anthropometrics, quality of life and sleep, as well as metabolic and hormonal parameters, at baseline (T0) and 180 days (T180) post-supplementation. Results demonstrated gut microbiota reshaping at phyla, genera, and species level post-supplementation. The Bacteroidetes phylum, Bacteroides, and Prevotella genera were positively modulated especially in the NSupple_Silybum group. Gut microbiota modulation was associated with improved sleep patterns, quality-of-life perception, cytokines expression, and anthropometric parameters post-supplementation. Our findings suggest that the nutraceutical blends positively enhance cardiometabolic and inflammatory markers. Particularly, NSupple_Silybum modulated microbiota composition, underscoring its potential significance in ameliorating metabolic dysregulation. Clinical trial registry number: NCT04810572. 23/03/2021.
Similar content being viewed by others
Introduction
Gut health1, a balanced diet2,3, and a good quality of life are meaningful and interlaced1. Several factors play a crucial role in physical and mental well-being, contributing to a good quality of life. Adequate sleep4, a healthy diet5, and regular physical activity6 are essential components, besides genetic7, and environmental factors8. These elements collectively contribute to fostering a high quality of life.
Managing these factors often requires substantial lifestyle changes. However, keeping a healthy lifestyle is increasingly challenging nowadays, especially regarding diet and good sleep quality9. External factors, such as disruptions in the body's internal clock10, over-stimulating environments11,12, dietary habits13,14,and gut dysbiosis15,16,17, might directly affect sleep quality. Furthermore, intrinsic factors such as the neuro-immune-endocrine axis, which encompasses neuroendocrine elements like the hypothalamic–pituitary–adrenal (HPA) axis, and the enteric nervous system (ENS), along with immune factors typified by the sympathoadrenal axis (gut-lymphoid connection)18; play a crucial role in regulating sleep quality, immune response, and hormonal balance15,19. These factors interact with the intestinal microbiota, further influencing various physiological functions.20,21.
While asleep, critical immune system repair and regeneration processes occur20,21. Imbalances in the immune system, such as chronic inflammation, unbalanced IL-6 and TNF-α levels, and cortisol levels15,19,22, can lead to sleep disorders20. Properly managing stress hormones like cortisol is also a key factor in promoting good sleep patterns, since cortisol levels increase during the night, falling after 9 a.m. Thus, the inflammatory and hormonal disturbances caused by chronic stress can harm sleep quality and lead to sleep disorders23. High levels of IL-6 and cortisol contribute to disrupting the homeostasis of the circadian cycle, as their secretions are intricately linked to circadian cycle regulation22. Chronic stress is known as a risk factor for developing metabolic and mood disorders like anxiety and depression24. Research findings indicate that the gut microbiota is crucial in the onset and progression of inflammatory and psychiatric disorders, likely through gut microbiota-brain interactions and neuroinflammation25. The gut microbiota interacts with the central nervous system (CNS) via the gut-brain axis, impacting sleep regulation15. Microbial byproducts with anti-inflammatory potential like short-chain fatty acids (SCFAs) as well as microbiota-derived neurotransmitters like serotonin, acetylcholine, histamine, norepinephrine, dopamine, and gamma-aminobutyric acid26, act on the microbiota-gut-brain axis and are involved in the sleep pathways; thus imbalances in gut microbiota composition may trigger sleep disturbances or disorders27,28.
Regarding the gut microbiota composition, it is crucial to highlight the reduction in Bacteroidetes phylum abundance associated with sleep disorders17 and the enhancement of sleep quality28,29. Especially the Bacteroides and Prevotella genera abundance has been associated with an improvement in metabolites renowned as good sleep quality promoters16,17,29,30. Also, the Alistipes onderdonkii species, included in the Bacteroidetes phylum, has recently gained attention as a microbiota marker associated with inflammation and mental health implications, besides its role in cardiometabolic protection30,31.
Acknowledging these complexities, efforts to find promoters of long-term quality of life have emerged. These non-pharmacological strategies aim to enhance sleep and dietary quality by favoring gut microbiota balance. Notably, certain nutrients32,33 and natural compounds seem to play a key role in promoting the microbiota-brain-endocrine axis. Silymarin shows antioxidant and anti-inflammatory effects closely linked to the increase of neurotransmitters like serotonin and dopamine, which are involved in improving depression symptoms34,35 that could contribute to better sleep quality34,35,36. Additionally, zinc and magnesium work as crucial cofactors in the conversion of tryptophan amino acids to serotonin, which is closely associated with welfare, directly affecting the quality of life perception and sleep quality37,38,39. Given modern dietary deficiencies in fiber and essential minerals crucial for balanced gut microbiota, continuous intake of prebiotic fibers such as β-glucans, GOS (galactooligosaccharides), and FOS (fructooligosaccharides) may represent a nutritional strategy to enhance mineral absorption, maintain intestinal health, and fortify gut microbiota40,41,42.
Therefore, this randomized clinical trial aims to explore the effects of a 180-day consumption of nutraceutical compositions containing three sources of prebiotics (β-glucans, GOS, and FOS), minerals (zinc, magnesium, and selenium), and herbal medicine (Silymarin extract—Silybum marianum L. Gaertn) This nutritional and nutraceutical supplement has been created as a tool for the microbiota-brain-immune-endocrine axis modulation, with its primary objective being the enhancement of sleep quality and an overall improvement in quality of life through the reshaping of gut microbiota in overweight volunteers from southeastern Brazil.
Results
Anthropometric and biochemistry effects
Before the beginning of supplementation, the volunteers were screened for age, gender, body mass, height, and BMI which did not differ between groups stating the sample homogeneity. Regarding the anthropometric data, only the NSupple reduced the neck circumference, and only the NSupple_Silybum reduced the waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) after supplementation. Both supplements displayed reduced waist circumference (WC-mid). The diet intake parameters did not differ (Table 1).
The serum parameters evaluated show that only NSupple reduced IgA, TSH (thyroid stimulating hormone), C-reactive protein (C-RP), cortisol levels, and increased fasting glycemia and cortisol/C-RP ratio. The glycated hemoglobin (HbA1c) and ALT (alanine aminotransferase) levels were increased in both groups. Moreover, both groups reduced AST/ALT ratio after supplementation as shown in Table 1.
Cytokines and chemokines modulation
The plasma cytokines and chemokines were accessed before and after supplementation, as shown in Table 2. The CCL5/RANTES, CXCL9/MIG, interferon-gamma (IFN-γ) levels, and IL-6/IL-10 ratio were reduced by both supplements after 180 days. Furthermore, after supplementation, both groups increased their interleukin 4 (IL-4) levels. The CXCL10/IP10 and IL12p70 levels increased solely in the NSupple group. The (tumor necrosis factor-alpha) TNF-α was reduced exclusively by the NSupple_Silybum supplement. The IL-6 level was increased in the NSupple group, while reduced in NSupple_Silybum. The CXCL8/IL-8, IL-1β, IL-10 levels, and TNF-α/IL-10 ratio did not differ.
Sleep, mood, and quality of life effects
Volunteers were screened for chronotype before the supplementation establishing the majority as Intermediate chronotype. Concerning sleep quality, both supplements reduced the Epworth Sleepiness Scale (ESS) score. However, only the NSupple group reduced the Mini-Sleep Questionnaire (MSQ-BR) score after supplementation. Evaluating the Pittsburgh Sleep Quality Index (PSQI), the NSupple improved sleep quality (C1) with greater sleep latency (C2) scores. Also, exclusively the NSupple_Silybum reduced the Sleep medication use (C6) shown in Table 3. The competencies of PSQI, namely (C3) Sleep Duration, (C4) Sleep Efficiency, (C5) Sleep Disturbance, and (C7) Daytime Dysfunction as well as the PSQI Global score did not change post-supplementation (Table 3).
Accessing the WHO Quality of Life score (WHOQoL-Bref) both supplements improved the Overall quality of life and general health perception after 180 days. Also, the NSupple group increased the physical domain perception while NSupple_Silybum improved the psychological domain perception after supplementation. The Social relationships and Environment domains in WHOQoL-Bref did not differ throughout time. Evaluating the Brunel Mood Scale (BRUMS) only the NSupple_Silybum impacted vigor perception after supplementation, the other mood variables did not differ (Table 3).
Gut microbiota reshaping by the supplementation
After 180 days, the gut microbiota phyla composition (Fig. 1A) in volunteers who underwent NSupple_Silybum supplementation demonstrated a relative decrease in Firmicutes and an increase in Bacteroidetes (Fig. 1B). Additionally, there was a noticeable reduction in the F/B ratio (Fig. 1C).
The analysis of the genera profile in both groups has demonstrated a significant influence on the gut microbiota composition (Fig. 2A). In the LEfSe analysis, we identified that both supplements modulated eight distinct bacterial features, each achieving a Linear Discriminant Analysis (LDA) score higher than two (Fig. 2B). The NSupple_Silybum group appeared to markedly increase the abundance of statistically significant genera such as Bacteroides and Prevotella, while decreasing R. Ruminococcus, Lachnospira, and Dialister (Fig. 2C). To facilitate the comprehension of microbiome data, we utilized a heatmap visualization technique, specifically emphasizing the phylum and genera that displayed differential representation between the supplemented groups (Fig. 2D).
Among the alpha (α)-diversity indices, Shannon and Faith’s PD displayed a statistical reduction in microbial abundance within the NSupple_Silybum group post-supplementation (Fig. 3A). Additional (α)-diversity indices were computed; however, no statistically significant differences were observed. These results are provided in the supplementary material for further reference (Fig. S1). Similarly, there were no statistical differences in the realm of beta (β)-diversity assessment, which encompassed Jaccard, Bray–Curtis Unweighted UniFrac, and Weighted UniFrac distances. These findings were visually represented through the Principal Coordinate Analysis Plot (PCoA) (Fig. 3B).
In terms of the supplements' impact on the microbiota species abundance, the NSupple_Silybum group showed a discernible influence by increasing the abundance of specific gut microbiota species, including Bacteroides uniformis, Alistipes onderdonkii, and Bacteroides caccae. Concurrently, there was a decrease observed in Roseburia faecis, Blautia obeum, Dorea formicigenerans, and Coprococcus eutactus (Fig. 3C).
Predictive parameters of microbiota modulation
The logistic regression data in Table 4 shows that improvement of WC-IC was associated with an increase in Bacteroidetes phyla and Bacteroides genus abundance in the NSupple_Silybum group. Also, ESS improvement was associated with the enrichment of Faecalibacterium in the NSupple_Silybum group.
The multiple linear regression analysis shows the association of a decrease in the AST/ALT ratio with reduced Bacteroidetes phyla (Coef 0.43) and a 3% increase in Sutterella genus abundance in the NSupple group. Still in the NSupple group, a decrease in ESS score was associated with reduced Bacteroides genus (Coef − 0.32) as well as increased Collinsella genus (Coef 0.58) and L. Ruminococcus species (Coef 0.42). The increased Overall QoL and General health perception were associated with the raised Faecalibacterium genus (Coef 0.08) in the NSupple group. Also, the reduction in IL-6/IL-10 ratio was associated with increased Bacteroides caccae (Coef 0.14) species in the gut microbiota of the NSupple group as shown in Table 4.
Regarding the NSupple_Silybum group, both anthropometric parameters reduction of WC-IC and WHtR were associated with the same microbiota genera modulation. For each centimeter of reduction in WC-IC there was a 0.32% reduction in the Bacteroides genus abundance; an 8.09% increase in the Dorea genus; a 1.29% increase in the Roseburia genus; and a 1.89% increase in the Sutturella genus. Similarly, the reduction in WHtR was associated with a reduction in the abundance of the genus Bacteroides (Coef − 0.002), and an increase in the genera Dorea (Coef 0.05), Roseburia (Coef 0.008) and Sutturella (Coef 0.012).
In the NSupple_Silybum group, the AST/ALT ratio reduction was also associated with a 19.49% decrease in the Phascolarctobacterium genus and a 7.62% increase in the abundance of Eubacterium biforme. Moreover, the improvement of ESS has been associated with a 2.57% increase in Alistipes onderdonkii species. Also, the rise in Overall QoL and General health perception was associated with a 0.57% increase in the Bilophila genus in the NSupple_Silybum group (Table 4).
Discussion
The advancement of delving natural supplements focused on supporting long-term health is progressively expanding. Maintaining a well-balanced state of health might lower the long-term risk of illness and has encouraged the exploration of well-being promoter therapies43. Recently, research on gut microbiota modulation has gained prominence by applying prebiotics, probiotics, phenolic compounds, macronutrients, and micronutrients attempting to achieve the ideal microbiota for health44. Recently, the gut microbiota has emerged as a pivotal regulator of several physiological systems, including the neuro-immune-endocrine axis, among others. Given its wide impact on health, alterations in the microbiota, like dysbiosis, might trigger metabolic issues like obesity and type 2 diabetes, as well as, sleep disorders, and a significant disturbance in mood and quality of life28. This has led to an increasing chase for comprehensive investigations into the effects of new combinations of nutrients on gut health. Thus, this clinical trial investigates a specific blend of nutrients, comprising prebiotics (β-glucans, GOS, and FOS), essential minerals (zinc, magnesium, and selenium), and an herbal medicine extract (Silymarin from Silybum marianum L. Gaertn., Asteraceae). on metabolic, endocrine, and behavioral enhancements associated with gut microbiota adaptability.
The methodological approach aimed to characterize the gut microbiota among overweight and sedentary volunteers living in southeastern Brazil before and after their intake of two different nutraceutical supplements over 180 days. Hence, we assessed whether the microbiota alterations could correlate with the improvement of chronic inflammation, sleep disorders, and gut dysbiosis commonly observed in chronic stress and metabolic diseases. Although our volunteers were categorized as overweight rather than obese, we observed enhancements in anthropometric measurements in the NSupple_Silybum post-supplementation linked to sleep disturbances, and central adiposity. The literature shows a close relationship between the improvement of anthropometrics, metabolic markers, sleep duration45, and obstructive sleep apnea syndrome46. The results presented here are promising initially showing the potential of the nutraceutical blend as a modulator of endocrine, metabolic, and anthropometric parameters without diet or exercise interventions.
It's noteworthy that the decrease in anthropometric parameters aids as indicators of a decreased cardiometabolic risk as well as in sleep patterns change. The reduction in WC-mid in both groups, neck circumference in NSupple, WHR, and WHtR in NSupple_Silybum depict visceral adiposity, which is widely recognized as more beneficial for metabolic health than simply reducing overall body mass or BMI. These findings are even more interesting if we highlight the absence of lifestyle modifications during supplement consumption. It allows us to suggest a lower risk of metabolic syndrome, type 2 diabetes, cardiovascular diseases, and other metabolic disorders among overweight people under supplementation47. Liu et al. 2017 have shown that FOS and GOS supplementation impaired fasting glucose and glycemic response in OGTT, which might relate to increased HbA1c, using machine learning and LEfSe analysis. The authors state that FOS and GOS might favor the microbiota modulation hindering the butyrate-producer genera such as Ruminococcus and Coprococcus48 which agrees with our microbiota findings. Butyrate, a short-chain fatty acid produced by microbiota, might improve type 2 diabetes features by increasing glucagon-like peptide-1 (GLP-1), an incretin hormone that participates in glucose homeostasis recovering fasting glycemia, insulin resistance, and inflammation49. Thus, the literature allows us to suggest that the prebiotics in our supplementation reduced butyrate production by the microbiota reshape leading to an increase in fasting glycemia and HbA1c. Nevertheless, it is important to note that prebiotics such as FOS and GOS have a dubious impact on glucose metabolism requiring in-depth research50,51. Despite a rise in fasting glycemia in the NSupple group and HbA1c in both groups post-supplementation, the parameters remained under the reference values range, thus not rendering the use of the supplements unfeasible.
Confirming the positive metabolic outcomes, in this clinical trial we note the improvement of liver function by a decrease in the AST/ALT ratio in both supplementation groups. These findings agree with the metabolic enhancements observed in volunteers reducing the onset of metabolic disorders alongside body changes52,53, these results boost the applicability and potency of the supplement blend tested. Also, prior preclinical investigations on this nutrient composition showcased a similar hepatoprotective effect, characterized by diminished liver enzymes and AST/ALT ratio. The preclinical model also provided insight into this hepatoprotective action, attributing it to increased mitochondrial biogenesis activity in the liver reducing metabolic dysfunction-associated fatty liver disease (MAFLD) in the mouse model54. This evidence strongly suggests a parallel with our clinical trial, where the improvement in the AST/ALT ratio, and the decrease in visceral adiposity measurements could be linked to the mitochondrial activity promoted by the supplementation. Enhanced mitochondrial activity provides a compelling rationale for the reduction in these metabolic and anthropometric parameters without alterations in dietary habits or an increase in physical activity.
These endocrine and metabolic factors are linked to alterations in sleep patterns, potentially resulting in sleep disorders that directly influence individual perception of quality of life and mood throughout the day, which might hinder performance in daily activities55. Our results have shown improvement in sleep quality perception by the MSQ and enhanced sleep quality and latency in PSQI scores in the NSupple group post-supplementation. Also, both groups reduced day sleepiness as shown in ESS. These results might impact the improved perception of overall quality of life and general health in both groups especially related to physical in the NSupple group and psychological aspects in the NSupple_Silybum group. All these changes may be connected since cross-sectional studies indicate a noteworthy inverse relationship between sleep duration and waist circumference and WHR (waist-to-hip ratio), suggesting that shorter durations of sleep tend to coincide with central adiposity45.
Obesity and high visceral adiposity markers are recognized as risk factors for sleep disorders such as obstructive sleep apnea syndrome56 an important sleep disorder that affects the quality of life and promotes pro-inflammatory cytokines expression (interleukin 6/IL-6 and tumor necrosis factor-α/TNF-α) impacting metabolic diseases onset46,57,58,59. These conditions involve changes in serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST), as well as nonalcoholic fatty liver disease (NAFLD). This association is tied to the chronic, intermittent periods of low oxygen levels during apnea episodes, leading to liver inflammation and fibrosis, closely linking sleep disorders and metabolic alterations60.
Sleep disorders are also associated with high cortisol levels61 which contribute to immune and inflammation dysregulation of pro-inflammatory factors such as IL-6, TNF- α, and C-reactive protein (C-RP). In sleep disorders such as insomnia, the increase in cortisol may be a marker of CRH (Corticotropin-releasing hormone) and norepinephrine activity during the night, resulting in non-restorative sleep and prolonged wakefulness61. Our results show the reduction in pro-inflammatory markers like C-RP and cortisol in the NSupple group demonstrating a positive effect on biological stress markers related to sleep quality62. Also, the Cortisol/C-RP ratio serves as a valuable marker for assessing the negative feedback loop between the hypothalamic–pituitary–adrenal (HPA) axis and the inflammatory response system in major mood disorders like depression and anxiety. Studies on mood disorders indicate that individuals exhibiting high depression symptoms have a reduced CORT/C-RP ratio—suggesting inadequate cortisol release relative to elevated C-RP and tend to experience worse stress-induced negative mood reactions63,64. Thus, even though our sample did not present mood disorders diagnostics, the increase in the Cortisol/C-RP ratio in the NSupple group might be suggested as a positive secondary outcome of the supplementation on mood disturbs reflecting on overall quality of life perception65.
During the study, the volunteers consistently maintained an approximate intake of 50% of the recommended daily fiber amount, ranging between 25 and 50 g66. Notably, this percentage remained unchanged even upon the inclusion of the supplements. Recognizing the pivotal role of sustaining the health and equilibrium of the intestinal microbiota, our focus has pivoted towards acknowledging nutrients and their derivatives as key elements for enhancing the gut microbiome.
Prebiotics fibers and flavonolignans in the Silybum marianum (L.) Gaertn. (silymarin) demonstrate direct efficacy in improving the gut physiology, microbiota diversity, and abundance in a metabolic syndrome mice model15. Prebiotic fibers are known for their benefits in providing energy and improving the microbiota diversity and abundance in mice67 and humans68,69, however always in high doses. Anon, the flavonolignans show a “phytotherapic prebiotic” effect by its chemical structure. Flavonoids, and lignans, provide carbohydrates, amino acids, and other compounds for microbiota fermentation and metabolism, like prebiotic fibers70,71,72,73. Furthermore, minerals such as zinc74, magnesium, and selenium75 serve as vital cofactors in fortifying the intestinal epithelium and immune system, thereby influencing various metabolic pathways conducive to enhancing intestinal health.
The supplement NSupple_Silybum emerged as the most impactful in modulating the abundance of the intestinal microbiota, notably promoting homeostasis by eliciting a reduction in Firmicutes and an increase in Bacteroidetes phyla. Moreover, the NSupple_Silybum effectively lowered the Firmicutes-to-Bacteroidetes (F/B) ratio. Lněničková and colleagues showed an increase in the Bacteroides genus after silymarin supplementation in humans, which supports our findings72.
Specifically, the influence of NSupple_Silybum on the Bacteroidetes phylum extended to the Bacteroides genus, particularly when correlated with decreased WC-IC (waist circumference—iliac crest) measurements and WHtR (waist-to-height ratio). These findings underscore the anti-inflammatory and cardioprotective attributes, and with the microbiota abundance inherent in the silymarin-containing supplement, concurrently demonstrating an improvement in sleep quality, as previously mentioned. Notably, the Faecalibacterium genus has exhibited a linked association with enhanced sleep quality76, aligning with our results in the NSupple_Silybum group. A groundbreaking discovery of our investigation lies in delineating a robust association between Alistipes onderdonkii species, belonging to the Bacteroidetes phylum, and improved sleep quality results in the NSupple_Silybum group. Despite its classification as a commensal organism within the large intestine77, Alistipes onderdonkii has often been implicated in various disease states or intestinal disorders31.
Both supplement groups exhibited notable systemic anti-inflammatory effects following a 180-day supplementation period, characterized by a reduction in chemokines closely linked to metabolic and intestinal disorders. Specifically, levels of RANTES40, CXCL9/MIG78, IL-6, and the IL-6/IL-10 ratio, recognized markers associated with inflammatory diseases such as gastric cancer79, witnessed a decrease in both groups. Concurrently, there was an elevation in IL-4 in both supplement groups, renowned for its anti-inflammatory properties80. This modulation indicates a propensity towards mitigating inflammatory responses.
Nevertheless, NSupple_Silybum showcased more pronounced anti-inflammatory attributes in this regard. It remarkably decreased the levels of TNF-α, which is linked to sleep disorders. Furthermore, NSupple revealed a robust association between the higher presence of Bacteroides caccae, a gut commensal species able to degrade host-derived mucin glycans producing short-chain fatty acids (SCFAs)81, known for the anti-inflammatory effect, intestinal barrier recovery82, safeguarding the intestinal barrier83, and the reduction in the IL-6/IL-10 ratio. These anti-inflammatory properties reveal the supplement's therapeutic efficacy and hint at their differential impacts on specific inflammatory pathways and associated physiological responses.
When scrutinizing the effects of supplements concerning genera-based disparities, discernible positive impacts emerged. Prevotella is typically linked to diets abundant in plant-derived fibers, non-western carbohydrate profiles84, and flavonoid fermentation85, which might be related to the NSupple_Silybum effect. This genus, known for its anti-obesogenic attributes through propionate production86, demonstrated marked benefits following the administration of NSupple_Silybum. Among the 16 genera manifesting statistically significant differences, the intervention with NSupple accounted for alterations in merely 4 genera. In contrast, NSupple_Silybum displayed a substantial influence, modulating 12 of these genera. This highlighted the significant impact of flavolignan within NSupple_Silybum on the overall microbial composition. Our research delved into the isolated effects of Silymarin, as elucidated in our previous preclinical study. However, when administered in isolation, Silymarin failed to replicate the effects witnessed when it was combined with other nutrients within the supplement composition42. This underscores the symbiotic interplay among various components, emphasizing their collective contribution to the achieved outcomes. It reiterates the significance of the holistic synergy among supplement constituents in yielding positive effects.
In the evaluation of bacterial species, it's essential to initially recognize their predominantly commensal nature, acting as holobionts, essential for environmental eubiosis. Dysbiosis often facilitates opportunistic or pathogenic species growth. However, it's crucial to acknowledge that this classification is reliant on predictive scenarios, heavily influenced by the specific strain's attributes to determining a species as a probiotic or opportunist. In our study, we observed significant modulation of two pivotal species by NSupple_Silybum. An increased abundance of Bacteroides uniformis, a species renowned for its metabolic and immunomodulatory functions87, and a reduced abundance of Clostridium clostridioforme were observed in NSupple_Silybum group. Although considered commensal, C. clostridioforme is acknowledged as a reservoir for antimicrobial resistance88.
Nevertheless, several limitations were notable within our study. Firstly, the absence of a placebo control group restricted our ability to effectively discern the specific effects of the supplements administered, isolating the placebo effect. Secondly, the study grappled with a limited sample size, compromising the statistical power and generalizability of the findings. Thirdly, an absence of comprehensive metabolomic analysis on the fecal samples hindered our capacity to elucidate the intricate pathways and mechanisms contributing to the observed effects. These limitations must be considered in future studies addressing other populations and, in-depth mechanisms of action investigations.
Despite these limitations, the results underscored the potential of the NSupple_Silybum supplement. Notably, it enhances sleep quality and mitigates inflammation, primarily through the modulation of bacteria within the Bacteroidetes phylum. These initial findings suggest the supplement like a promising tool for preventive and therapeutic strategies in chronic non-communicable diseases and sleep disturbance through gut microbiota modulation.
Methods
Ethics committee approval
This research adhered to the Declaration of Helsinki and was formally approved by the Ethics Committee for the Analysis of Research Projects (CAPPesq) from the HC-FMUSP Research Ethics Committee under CAAE number 39984320.5.0000.0068. This study has been approved by the Brazilian National System of Genetic Registration (SisGen) under registration number AC29D69. Additionally, it was registered under the identification number NCT04810572 at ClinicalTrials.gov on 23/03/2021.
Enrollment of participants and experimental design
This clinical trial recruited 133 volunteer residents in the southeastern region of Brazil, at the outpatient clinic “Ambulatório Monte Azul” (São Paulo, Brazil), and through online advertisement from 03/01/2021 to 06/09/2021. The trial data collection ended on 12/20/2021. This double-blind, randomized trial evaluated participants at baseline (T0) and after 180 days of supplementation (T180). The trial adhered to pre-established protocols for inclusion/exclusion criteria, and supplementation previously described by Nehmi-Filho et al., (2023)89. Briefly, the exclusion criteria were: use of insulin injection, corticoids, and non-steroidal anti-inflammatory drugs for more than 15 days; AIDS or hepatitis diagnosis; pregnancy; and patients under chemotherapy treatment. For volunteers who met the research inclusion criteria, we applied stratified randomization considering factors such as age, sex, and BMI. The Consolidated Standards of Reporting Trials (CONSORT)90 flow chart outlines the trial's progression in Fig. 4. In summary, 133 individuals initially responded to the volunteer call. Subsequently, 42 individuals withdrew before randomization because they did not meet the inclusion criteria (n = 9) or declined to participate further (n = 33). Consequently, 91 individuals were randomized into two experimental groups: NSupple (n = 47) and NSupple_Silybum (n = 44) at T0. Throughout the 180-day supplementation period, 26 volunteers from the NSupple group were excluded due to time constraints (n = 7), COVID-19 infection (n = 2), poor sample quality (n = 9), or by no alleged reasons (n = 8). Similarly, 24 volunteers from the NSupple_Silybum group withdrew because of time constraints (n = 4), COVID-19 infection (n = 1), poor sample quality (n = 9), or unspecified reasons (n = 10). Consequently, by the end of the experiment, samples from 21 individuals in the NSupple group and 20 individuals in the NSupple_Silybum group which completed the 180 days of supplementation were analyzed. Blood and fecal samples were collected at both T0 and T180. All participants provided free and informed consent before participation.
Nutraceutical composition formulas
Two different formulations were tested herein (Patent number BR 10 2020 016156 3) as previously published by Nehmi-Filho, Santamarina, et al., 202389 and described below. NSupple: zinc (Zn) 1%, magnesium (Mg) 1% (Purifarma Distribuidora Química e Farmacêutica, São Paulo, Brazil), Fructooligosaccharide (FOS) 45% (NutraFlora®, Westchester, Illinois, USA), selenomethionine (Se) 0.01%, Galactooligosaccharide (GOS) 10%, tixosil 5%, and 1.3/1.6-(β-glycosidic bonds) yeast β-glucans (Saccharomyces cerevisiae) 6% (Biorigin, São Paulo, Brazil); and NSupple_Silybum: zinc (Zn) 1%, magnesium (Mg) 1% (Purifarma Distribuidora Química e Farmacêutica, São Paulo, Brazil), Fructooligosaccharide (FOSs) 45% (NutraFlora®, Westchester, Illinois, USA), selenomethionine (Se) 0.01%, Galactooligosaccharide (GOS) 10%, tixosil 5%, 1.3/1.6-(β-glycosidic bonds) yeast β-glucans (Saccharomyces cerevisiae) 6% (Biorigin, São Paulo, Brazil), and Silybum marianum (3.11% of seed extract) (SM Empreendimento Farmacêutica LTDA, São Paulo, Brazil). The “Solis Magistral Farmácia Homeopatia Sensitiva” (São Paulo, Brazil) team was responsible for the study blinding, and preparing the formulations following EFSA guidelines91.
The absence of a placebo group might be unusual in the experimental design of a clinical trial, however, the literature discusses that the need for a placebo is pertinent only to drug trials92. Nevertheless, most clinical trials usually apply a placebo to facilitate outcome blinding and to control the placebo effect. However, especially research on gut microbiota must be careful with placebo application since most common placebos applied interact with the gut microbiota which might represent a bias. Starch, resistant starch, cellulose, sugar, and maltodextrin are common placebos generally considered inert for humans. However, they may be metabolized reshaping gut microbiota, producing metabolites used by host intestinal cells, and reaching the bloodstream, potentially offering beneficial effects93,94,95,96,97,98,99,100,101. Based on our literature review, there is no ideal placebo that did not interact with bacteria in microbiota studies93,94,95,96,97,98,99,100,101. Thus, we opted to avoid the placebo, using individual self-control instead, by comparing before and after supplementation in paired analyses.
Volunteers’ anthropometrics
Anthropometric measurements were assessed at the initial time point (T0) and post-supplementation (T180). The volunteers' body mass and height were measured using the Body Composition Scale 2 (Xiaomi Mi, Beijing, China). Circumferences of the neck (cm), and waist (cm), were gauged using a plastic tape measure. Body Mass Index [BMI = body mass (kg)/height (m)2], waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) were calculated. The hip (cm) and iliac crest (cm) circumferences were measured and presented in supplementary data (Table S1).
Serum parameter
Blood samples obtained at T0 and T180 between 7:00 a.m. and 9:00 a.m. underwent analysis for glycemia, HbA1c, IgA, albumin, C-reactive protein (C-RP), ALT, TSH, and cortisol. The AST/ALT ratio and Cortisol/C-RP were calculated. Additionally, Insulin, HOMA-IR, total cholesterol and fractions (HDL, LDL, VLDL, non-HDL), triglycerides, IgG, IgM, total protein, AST, alkaline phosphatase, creatinine, gamma-GT, and thyroxine were investigated and presented in supplementary data Table S1. These analyses were conducted by the "Fleury Medicina e Saúde" laboratory (São Paulo, Brazil).
Dietary intake and physical activity data
Participants' dietary intake data were obtained at T0 and T180 from a three-day food diary and analyzed using DietPro software (version 6.1). Physical activity was assessed through the International Physical Activity Questionnaire (IPAQ), categorizing activities based on intensity102.
Cytokines and chemokines levels
The cytometric bead array (CBA) test was employed to quantify cytokine and chemokines in plasma samples stored at − 80 °C. Beads, standards, reagents, and plasma samples were prepared according to the manufacturer's guidelines. Results were obtained using the BD Accuri flow cytometer and commercially available kits (BDTM Cytometric Bead Array) and commercial kits from BD™ Cytometric Bead Array (CBA) for “Human Chemokine” and “Human Inflammatory Cytokines” were used (BD Biosciences, USA). Standard dilution methods were applied for cytokine standards. Calibration of the flow cytometer with cytometer setup beads preceded the assay. Data analysis was performed in FCAP Array v2.0 software (SoftFlow, Pecs, Hungary), presenting results in pg/mL.
Questionnaires for sleep, mood, and quality of life characterization
The Brazilian Portuguese Version of the Mini-Sleep Questionnaire (MSQ-BR) was used to assess the subjective quality of sleep103. Epworth sleepiness scale (ESS) was applied to measure the general daytime sleepiness level104. The Portuguese version of the Horne and Ostberg morningness-eveningness questionnaire was used to establish the volunteers' chronotype105.
The Pittsburgh Sleep Quality Index (PSQI)103 was applied to assess sleep quality and disturbances over 1 month. To assess the participants’ perception of quality of life, we applied the “World Health Organization quality of life instrument-short form” (WHOQoL-BREF)106. Brunel Mood Scale (BRUMS) was used as a validated instrument to screen the volunteer's mood perception107.
Microbiome analysis
Sample collection and genomic extraction
Each participant collected approximately 1 g of feces and placed it in a guanidine stool preservation medium, to preserve the microbiome promptly after collection. The samples were maintained under temperature control, ranging from 2 to 8 °C during transport and 0 to − 80 °C during storage108. Genomic samples were obtained by extracting genomic material using about 0.25 g of feces and the DNeasy PowerSoil Kit (Qiagen, Germantown, MD, USA). The extracted material was then stored at − 20 °C until the library preparation phase.
Library preparation and sequencing
The library preparation and sequencing procedures are extensively detailed by Nehmi-Filho, et al.42. In summary, for prokaryotic community analysis, 16S rRNA (V4 region) sequences were directly amplified and sequenced using 515F/806R. This amplification was carried out using a bacterial/archaeal primer set, specifically 515F/806R109. The sequencing process was performed according to the manufacturer's instructions (Thermo Fisher Scientific, Waltham, Massachusetts, USA) using the Ion Chef System and the Ion S5 platform.
Bioinformatic analysis
The detailed bioinformatic analysis was previously described by Nehmi-Filho, et al.42. Briefly, the 16S rRNA gene data underwent preprocessing and diversity estimation using Quantitative Insights Into Microbial Ecology (QIIME 2) version 2020.11110. The average number of sequences per sample was 52,389. The data were denoised with DADA2 (via q2‐dada2) using default parameters, which included a length threshold of 200 bp and an average quality Phred score of ≥ 30. This denoising step generated amplicon sequence variants (ASVs)111. The analysis identified 3428 ASVs. Following the construction of a phylogenetic tree, alpha and beta diversity metrics were calculated using Q2-diversity. The samples were rarefied to 19,803 sequences per sample112 before estimating these metrics.
The taxonomic classification of ASVs was performed using the Q2‐feature‐classifier113, specifically employing the naive Bayes classifier against the Greengenes 13_8 99% OTUs (Operational Taxonomic Unit) reference sequences114. The composition of microbiota communities was summarized at various taxonomy levels, including species, genera, families, orders, classes, and phyla ranks.
Additionally, alpha diversity metrics such as Chao1, Simpson, OTUs, Pielou's evenness, Shannon diversity, and Faith's phylogenetic diversity were calculated. The beta diversity metrics employed were Jaccard distance, Bray–Curtis distance, Unweighted and Weighted UniFrac distances. To enhance the interpretability of microbiome data, we employed a heatmap visualization technique focusing on phyla, genera, and species. Differential abundance analysis was conducted utilizing the R (4.3.1) package DESeq2 (1.42.0).
Statistical analysis
The sample size was determined using G*Power 3.1 software115, assuming a T-test (Means: Difference between two dependent means-matched pairs) with (α) level of 0.05, a power (β) of 0.90, and an effect size (success rate) of 0.5. The calculated total sample size required was 36 volunteers. Considering potential dropouts, we increased the sample size by 20%, resulting in an ideal sample size of 43 volunteers to ensure data reliability. Data were classified as parametric or nonparametric based on the Shapiro–Wilks and Smirnov-Kolmogorov test and outliers were excluded by the Grubbs test. Continuous parametric data were shown as mean ± standard deviation, and nonparametric as median and interquartile range. Continuous parametric variables were performed Student T-Test and, for nonparametric variables, the Wilcoxon test was applied. Linear and logistic regression tests were also performed for continuous and categorical variables, respectively, to verify all variables associated with the outcomes of interest investigated in this study. For the linear regressions, the magnitudes of the differences between the baseline and final results were stipulated for each variable of interest in comparison with the dependent variable. For the logistic regressions, the normality thresholds were verified for each variable of interest when present and researched in the scientific literature, or when absent, the lower and upper thresholds were defined to the 50% quartile for all variables to be investigated. Analyses were performed using STATA® 16-SE (Stata Corp. LCC, College Station, TX, USA) and GraphPad Prism 9.0 (GraphPad Software, La Jolla, CA, USA) software. The genera that were differentially represented between supplement groups were determined using the R (4.3.1) package DESeq2 (1.42.0). Differential gene expression analysis based on the negative binomial distribution116. To determine the taxonomic characteristics most likely to explain differences between periods and supplement groups, we employed the algorithm Linear discriminant analysis Effect Size (LEfSe 1.1.2)117. For all analyses, significance was determined as p ≤ 0.05.
Data availability
The data that support the findings of this study are available from Ana Flávia Marçal Pessoa but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Ana Flávia Marçal Pessoa. The datasets generated and/or analyzed during the current study are available in the GenBank ® repository, Bioproject PRJNA941000. Link: https://www.ncbi.nlm.nih.gov/sra/PRJNA941000 (Release date: 06/30/2025). The data that support the findings of this study are available on request from the corresponding author. This link is exclusive for the reviewers: https://dataview.ncbi.nlm.nih.gov/object/PRJNA941000?reviewer=lfevhd0p2jn9358imrei528946.
References
Swarte, J. C. et al. Health-related quality of life is linked to the gut microbiome in kidney transplant recipients. Nat. Commun. 14, 7968 (2023).
O’Neil, A. et al. A randomised, controlled trial of a dietary intervention for adults with major depression (the “SMILES” trial): Study protocol. BMC Psychiatry 13, 114 (2013).
Brushett, S. et al. Healthy diets positively associated with health-related quality of life in children and adolescents from low socioeconomic areas: Findings from the Greek Food Aid Program, DIATROFI. Nutrition 121, 112367 (2024).
Dalmases, M. et al. Impact of sleep health on self-perceived health status. Sci. Rep. 9, 7284 (2019).
Pano, O. et al. Healthy diet, depression and quality of life: A narrative review of biological mechanisms and primary prevention opportunities. WJP 11, 997–1016 (2021).
Zapalac, K., Miller, M., Champagne, F. A., Schnyer, D. M. & Baird, B. The effects of physical activity on sleep architecture and mood in naturalistic environments. Sci. Rep. 14, 5637 (2024).
Han, B. et al. Genetic and non-genetic factors associated with the phenotype of exceptional longevity & normal cognition. Sci. Rep. 10, 19140 (2020).
Streimikiene, D. Environmental indicators for the assessment of quality of life. Intellect. Econ. 9, 67–79 (2015).
Friedman, S. M. Lifestyle (medicine) and healthy aging. Clin. Geriatr. Med. 36, 645–653 (2020).
Potter, G. D. M. et al. Circadian rhythm and sleep disruption: Causes, metabolic consequences, and countermeasures. Endocr. Rev. 37, 584–608 (2016).
Caddick, Z. A., Gregory, K., Arsintescu, L. & Flynn-Evans, E. E. A review of the environmental parameters necessary for an optimal sleep environment. Build. Environ. 132, 11–20 (2018).
Rafique, N. et al. Effects of mobile use on subjective sleep quality. NSS 12, 357–364 (2020).
Zuraikat, F. M., Wood, R. A., Barragán, R. & St-Onge, M.-P. Sleep and diet: Mounting evidence of a cyclical relationship. Annu. Rev. Nutr. 41, 309–332 (2021).
Karbasi, S. et al. The relationship between dietary patterns and insomnia in young women. Neuropsychopharm. Rep. 43, 228–238 (2023).
Smith, R. P. et al. Gut microbiome diversity is associated with sleep physiology in humans. PLoS ONE 14, e0222394 (2019).
Matenchuk, B. A., Mandhane, P. J. & Kozyrskyj, A. L. Sleep, circadian rhythm, and gut microbiota. Sleep Med. Rev. 53, 101340 (2020).
Wu, J. et al. Associations between gut microbiota and sleep: A two-sample, bidirectional Mendelian randomization study. Front. Microbiol. 14, 1236847 (2023).
Mayer, E. A. Gut feelings: The emerging biology of gut–brain communication. Nat. Rev. Neurosci 12, 453–466 (2011).
Rusch, J. A., Layden, B. T. & Dugas, L. R. Signalling cognition: The gut microbiota and hypothalamic-pituitary-adrenal axis. Front. Endocrinol. 14, 1130689 (2023).
Irwin, M. R. & Opp, M. R. Sleep health: Reciprocal regulation of sleep and innate immunity. Neuropsychopharmacology 42, 129–155 (2017).
Chu, C. et al. The microbiota regulate neuronal function and fear extinction learning. Nature 574, 543–548 (2019).
Vgontzas, A. N. et al. IL-6 and its circadian secretion in humans. Neuroimmunomodulation 12, 131–140 (2005).
O’Byrne, N. A., Yuen, F., Butt, W. Z. & Liu, P. Y. Sleep and circadian regulation of cortisol: A short review. Curr. Opin. Endocr. Metab. Res. 18, 178–186 (2021).
Lihua, M., Kaipeng, Z., Xiyan, M., Yaowen, C. & Tao, Z. Systematic review and meta-analysis of stress management intervention studies in patients with metabolic syndrome combined with psychological symptoms. Medicine 102, e35558 (2023).
Wang, Z. et al. Gut microbiota modulates the inflammatory response and cognitive impairment induced by sleep deprivation. Mol. Psychiatry 26, 6277–6292 (2021).
Strandwitz, P. Neurotransmitter modulation by the gut microbiota. Brain Res. 1693, 128–133 (2018).
Magzal, F. et al. Associations between fecal short-chain fatty acids and sleep continuity in older adults with insomnia symptoms. Sci. Rep. 11, 4052 (2021).
Neroni, B. et al. Relationship between sleep disorders and gut dysbiosis: What affects what?. Sleep Med. 87, 1–7 (2021).
Grosicki, G. J., Riemann, B. L., Flatt, A. A., Valentino, T. & Lustgarten, M. S. Self-reported sleep quality is associated with gut microbiome composition in young, healthy individuals: A pilot study. Sleep Med. 73, 76–81 (2020).
Mulder, D., Aarts, E., Arias Vasquez, A. & Bloemendaal, M. A systematic review exploring the association between the human gut microbiota and brain connectivity in health and disease. Mol. Psychiatry https://doi.org/10.1038/s41380-023-02146-4 (2023).
Parker, B. J., Wearsch, P. A., Veloo, A. C. M. & Rodriguez-Palacios, A. The genus alistipes: Gut bacteria with emerging implications to inflammation, cancer, and mental health. Front. Immunol. 11, 906 (2020).
Oriach, C. S., Robertson, R. C., Stanton, C., Cryan, J. F. & Dinan, T. G. Food for thought: The role of nutrition in the microbiota-gut–brain axis. Clin. Nutr. Exp. 6, 25–38 (2016).
Romaní-Pérez, M. et al. The microbiota and the gut-brain axis in controlling food intake and energy homeostasis. IJMS 22, 5830 (2021).
Thakare, V. N. et al. Therapeutic potential of silymarin in chronic unpredictable mild stress induced depressive-like behavior in mice. J. Psychopharmacol. 32, 223–235 (2018).
Ranjan, S. & Gautam, A. Pharmaceutical prospects of Silymarin for the treatment of neurological patients: An updated insight. Front. Neurosci. 17, 1159806 (2023).
Lu, P. et al. Silibinin attenuates cognitive deficits and decreases of dopamine and serotonin induced by repeated methamphetamine treatment. Behav. Brain Res. 207, 387–393 (2010).
Eby, G. A. & Eby, K. L. Magnesium for treatment-resistant depression: A review and hypothesis. Med. Hypotheses 74, 649–660 (2010).
Sarris, J. et al. Nutritional medicine as mainstream in psychiatry. Lancet Psychiatry 2, 271–274 (2015).
Siodłak, D., Nowak, G. & Mlyniec, K. Interaction between zinc, the GPR39 zinc receptor and the serotonergic system in depression. Brain Res. Bull. https://doi.org/10.1016/j.brainresbull.2021.02.003 (2021).
Ansari, N. et al. Comparison of RANTES expression in Crohn’s disease and ulcerative colitis: An aid in the differential diagnosis?. J. Clin. Pathol. 59, 1066–1072 (2006).
Haarhuis, J. E., Kardinaal, A. & Kortman, G. A. M. Probiotics, prebiotics and postbiotics for better sleep quality: A narrative review. Benef. Microbes 13, 169–182 (2022).
Nehmi-Filho, V. et al. Novel Nutraceutical (silymarin, yeast β-glucan, prebiotics, and minerals) shifts gut microbiota and restores large intestine histology of diet-induced metabolic syndrome mice. J. Funct. Foods 107, 105671 (2023).
Johnson, A. A., English, B. W., Shokhirev, M. N., Sinclair, D. A. & Cuellar, T. L. Human age reversal: Fact or fiction?. Aging Cell 21, e13664 (2022).
Khawar, M. M. et al. The gut-brain axis in autoimmune diseases: Emerging insights and therapeutic implications. Cureus https://doi.org/10.7759/cureus.48655 (2023).
Sperry, S. D., Scully, I. D., Gramzow, R. H. & Jorgensen, R. S. Sleep duration and waist circumference in adults: A meta-analysis. Sleep 38, 1269–1276 (2015).
Seidell, J. C. Waist circumference and waist/hip ratio in relation to all-cause mortality, cancer and sleep apnea. Eur. J. Clin. Nutr. 64, 35–41 (2010).
Huxley, R., Mendis, S., Zheleznyakov, E., Reddy, S. & Chan, J. Body mass index, waist circumference and waist:hip ratio as predictors of cardiovascular risk: A review of the literature. Eur. J. Clin. Nutr. 64, 16–22 (2010).
Liu, F. et al. Fructooligosaccharide (FOS) and Galactooligosaccharide (GOS) increase bifidobacterium but reduce butyrate producing bacteria with adverse glycemic metabolism in healthy young population. Sci. Rep. 7, 11789 (2017).
Puddu, A., Sanguineti, R., Montecucco, F. & Viviani, G. L. Evidence for the gut microbiota short-chain fatty acids as key pathophysiological molecules improving diabetes. Mediat. Inflamm. 2014, 1–9 (2014).
Dall’Oglio, F., Milani, M. & Micali, G. Effects of oral supplementation with FOS and GOS prebiotics in women with adult acne: The “S.O. Sweet” study: A proof-of-concept pilot trial. CCID 11, 445–449 (2018).
Hachul, A. C. L. et al. Oligofructose supplementation (10%) during pregnancy and lactation does not change the inflammatory effect of concurrent trans fatty acid ingestion on 21-day-old offspring. Lipids Health Dis. 12, 59 (2013).
Kakazua, V. A., Pinto, R. Z. & Dokkedal-Silva, V. Sleep quality, body mass index and waist-to-hip ratio in older adults. Phys. Occup. Ther. Geriatr. 40, 150–160 (2022).
Sun, D.-Q. et al. MAFLD and risk of CKD. Metabolism 115, 154433 (2021).
Santamarina, A. B. et al. The symbiotic effect of a new nutraceutical with yeast β-glucan, prebiotics, minerals, and silybum marianum (Silymarin) for recovering metabolic homeostasis via Pgc-1α, Il-6, and Il-10 gene expression in a type-2 diabetes obesity model. Antioxidants 11, 447 (2022).
Chasens, E. R. et al. Sleep and metabolic syndrome. Nurs. Clin. N. Am. 56, 203–217 (2021).
Muscogiuri, G. et al. Obesity and sleep disturbance: The chicken or the egg?. Crit. Rev. Food Sci. Nutr. 59, 2158–2165 (2019).
Eun, Y. G. et al. Short-term effect of multilevel surgery on adipokines and pro-inflammatory cytokines in patients with obstructive sleep apnea. Acta Oto-Laryngol. 130, 1394–1398 (2010).
Unal, Y., Ozturk, D. A., Tosun, K. & Kutlu, G. Association between obstructive sleep apnea syndrome and waist-to-height ratio. Sleep Breath 23, 523–529 (2019).
Valmorbida, A., Longo, G. Z., Nascimento, G. M., de Oliveira, L. L. & de Moraes Trindade, E. B. S. Association between cytokine levels and anthropometric measurements: A population-based study. Br. J. Nutr. 129(7), 1119–1126 (2023).
Sookoian, S. & Pirola, C. J. Obstructive sleep apnea is associated with fatty liver and abnormal liver enzymes: A meta-analysis. Obes. Surg. 23, 1815–1825 (2013).
Hirotsu, C., Tufik, S. & Andersen, M. L. Interactions between sleep, stress, and metabolism: From physiological to pathological conditions. Sleep Sci. 8, 143–152 (2015).
Park, S. et al. Effect of weekend catch-up sleep on high-sensitivity C-reactive protein levels according to bedtime inconsistency: A population-based cross-sectional study. Sci. Rep. 12, 21619 (2022).
Suarez, E. C. & Sundy, J. S. The cortisol:C-reactive protein ratio and negative affect reactivity in depressed adults. Health Psychol. 36, 852–862 (2017).
Sharpley, C. F., Bitsika, V., McMillan, M. E., Jesulola, E. & Agnew, L. L. The association between cortisol:C-reactive protein ratio and depressive fatigue is a function of CRP rather than cortisol. NDT 15, 2467–2475 (2019).
Yamazaki, E. M. et al. Cortisol and C-reactive protein vary during sleep loss and recovery but are not markers of neurobehavioral resilience. Front. Physiol. 12, 782860 (2021).
Slavin, J. L. Dietary fiber: Classification, chemical analyses, and food sources. J. Am. Dietet. Assoc. 87, 1164–1168 (1987).
Sajnaga, E. et al. Response of murine gut microbiota to a prebiotic based on oligosaccharides derived via hydrolysis of fungal α-(1→3)-d-glucan: Preclinical trial study on mice. Food Chem. 417, 135928 (2023).
Penumutchu, S., Korry, B. J., Hewlett, K. & Belenky, P. Fiber supplementation protects from antibiotic-induced gut microbiome dysbiosis by modulating gut redox potential. Nat. Commun. 14, 5161 (2023).
Aljuraiban, G. S. et al. Types of fiber and gut microbiota composition and diversity among Arab females. Saudi J. Biol. Sci. 30, 103767 (2023).
Peterson, C. T. et al. Prebiotic potential of herbal medicines used in digestive health and disease. J. Altern. Complement. Med. 24, 656–665 (2018).
Pilkington, L. I., Wagoner, J., Kline, T., Polyak, S. J. & Barker, D. 1,4-Benzodioxane Lignans: An efficient, asymmetric synthesis of flavonolignans and study of neolignan cytotoxicity and antiviral profiles. J. Nat. Prod. 81, 2630–2637 (2018).
Lněničková, K. et al. Metabolic profiling of silymarin constituents in urine and feces of healthy volunteers: A 90-day study. J. Funct. Foods 100, 105391 (2023).
Wang, H. et al. Weight loss promotion in individuals with obesity through gut microbiota alterations with a multiphase modified ketogenic diet. Nutrients 15, 4163 (2023).
Hu, X. et al. Aryl hydrocarbon receptor utilises cellular zinc signals to maintain the gut epithelial barrier. Nat. Commun. 14, 5431 (2023).
Ye, R., Huang, J., Wang, Z., Chen, Y. & Dong, Y. Trace element selenium effectively alleviates intestinal diseases. IJMS 22, 11708 (2021).
Wagner-Skacel, J. et al. Sleep and microbiome in psychiatric diseases. Nutrients 12, 2198 (2020).
Shkoporov, A. N. et al. Alistipes inops sp. nov. and Coprobacter secundus sp. nov., isolated from human faeces. Int. J. Syst. Evol. Microbiol. 65, 4580–4588 (2015).
Li, L. et al. MIG/CXCL9 exacerbates the progression of metabolic-associated fatty liver disease by disrupting Treg/Th17 balance. Exp. Cell Res. 407, 112801 (2021).
Zhou, L., Tang, C., Li, X. & Feng, F. IL-6/IL-10 mRNA expression ratio in tumor tissues predicts prognosis in gastric cancer patients without distant metastasis. Sci. Rep. 12, 19427 (2022).
Woodward, E. A., Prêle, C. M., Nicholson, S. E., Kolesnik, T. B. & Hart, P. H. The anti-inflammatory effects of interleukin-4 are not mediated by suppressor of cytokine signalling-1 (SOCS1). Immunology 131, 118–127 (2010).
Berkhout, M. D., Plugge, C. M. & Belzer, C. How microbial glycosyl hydrolase activity in the gut mucosa initiates microbial cross-feeding. Glycobiology 32, 182–200 (2022).
Desai, M. S. et al. A dietary fiber-deprived gut microbiota degrades the colonic mucus barrier and enhances pathogen susceptibility. Cell 167, 1339-1353.e21 (2016).
Zafar, H. & Saier, M. H. Gut Bacteroides species in health and disease. Gut Microbes 13, 1848158 (2021).
Prasoodanan, P. K. et al. Western and non-western gut microbiomes reveal new roles of Prevotella in carbohydrate metabolism and mouth–gut axis. npj Biofilms Microbiomes 7, 77 (2021).
Pan, L. et al. Effects of several flavonoids on human gut microbiota and its metabolism by in vitro simulated fermentation. Front. Microbiol. 14, 1092729 (2023).
Precup, G. & Vodnar, D.-C. Gut Prevotella as a possible biomarker of diet and its eubiotic versus dysbiotic roles: A comprehensive literature review. Br. J. Nutr. 122, 131–140 (2019).
López-Almela, I. et al. Bacteroides uniformis combined with fiber amplifies metabolic and immune benefits in obese mice. Gut Microbes 13, 1–20 (2021).
Dehoux, P. et al. Comparative genomics of Clostridium bolteae and Clostridium clostridioforme reveals species-specific genomic properties and numerous putative antibiotic resistance determinants. BMC Genom. 17, 819 (2016).
Nehmi-Filho, V. et al. Novel nutraceutical supplements with yeast β-glucan, prebiotics, minerals, and Silybum marianum (silymarin) ameliorate obesity-related metabolic and clinical parameters: A double-blind randomized trial. Front. Endocrinol. 13, 1089938 (2023).
Schulz, K. F., Altman, D. G. & Moher, D. CCoOrresNpoSndOenRceT 2010 Statement: Updated Guidelines for Reporting Parallel Group Randomised Trials (2010).
European Food Safety Authority (EFSA). Dietary Reference Values for nutrients Summary report. EFS 14, e15121 (2017).
Vickers, A. Why use placebos in clinical trials? A narrative review of the methodological literature. J. Clin. Epidemiol. 53, 157–161 (2000).
Zaman, S. A. & Sarbini, S. R. The potential of resistant starch as a prebiotic. Crit. Rev. Biotechnol. https://doi.org/10.3109/07388551.2014.993590 (2015).
Kadyan, S. et al. Resistant starches from dietary pulses modulate the gut metabolome in association with microbiome in a humanized murine model of ageing. Sci. Rep. 13, 10566 (2023).
Chen, Z. et al. Resistant starch and the gut microbiome: Exploring beneficial interactions and dietary impacts. Food Chem. X 21, 101118 (2024).
Moraïs, S. et al. Cryptic diversity of cellulose-degrading gut bacteria in industrialized humans. Science 383, eadj9223 (2024).
Fischer, F. et al. Dietary cellulose induces anti-inflammatory immunity and transcriptional programs via maturation of the intestinal microbiota. Gut Microbes 12, 1829962 (2020).
Kim, Y. et al. Dietary cellulose prevents gut inflammation by modulating lipid metabolism and gut microbiota. Gut Microbes 11, 944–961 (2020).
Calgaro, M. et al. Metabarcoding analysis of gut microbiota of healthy individuals reveals impact of probiotic and maltodextrin consumption. Benef. Microbes 12, 121–136 (2021).
Hu, J. et al. High-amylose corn starch regulated gut microbiota and serum bile acids in high-fat diet-induced obese mice. IJMS 23, 5905 (2022).
Almutairi, R., Basson, A. R., Wearsh, P., Cominelli, F. & Rodriguez-Palacios, A. Validity of food additive maltodextrin as placebo and effects on human gut physiology: Systematic review of placebo-controlled clinical trials. Eur. J. Nutr. 61, 2853–2871 (2022).
Matsudo, S. et al. Questionario Internacional De Ativi Dade Fisica (I PAQ)
Falavigna, A. et al. Consistency and reliability of the Brazilian Portuguese version of the Mini-Sleep Questionnaire in undergraduate students. Sleep Breath 15, 351–355 (2011).
Bertolazi, A. N. et al. Portuguese-language version of the Epworth sleepiness scale: Validation for use in Brazil. J. Bras. Pneumol. 35, 877–883 (2009).
da Silva, C. F. et al. The Portuguese version of the Horne and Ostberg morningnesseveningness questionnaire: Its role in education and psychology. Rev. Psicol. Educ. 1, 39–50 (2002).
Esch, L. V., Oudsten, B. L. D. & Vries, J. D. The World Health Organization Quality of Life Instrument-Short Form (WHOQOL-BREF) in women with breast problems. Int. J. Clin. Health Psychol. 11, 5–22 (2011).
Rohlfs, I. C. P. D. M. et al. A Escala de Humor de Brunel (Brums): Instrumento para detecção precoce da síndrome do excesso de treinamento. Rev Bras Med Esporte 14, 176–181 (2008).
Ribeiro, R. M. et al. An alternative storage method for characterization of the intestinal microbiota through next generation sequencing. Rev. Inst. Med. trop. S. Paulo 60, 77 (2018).
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U.S.A. 108, 4516–4522 (2011).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018).
Mandal, S. et al. Analysis of composition of microbiomes: A novel method for studying microbial composition. Microb. Ecol. Health Dis. 26, 27663 (2015).
Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191 (2007).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
Acknowledgements
The authors would like to thank Márcia Alves at Efeom Nutrition S.A., and the employees of the LIM26 for technical support.
Funding
This study received funding from Efeom Nutrition S.A. (01/04–21); Fundação de Amparo à Pesquisa do Estado de São Paulo, (FAPESP 18/14389–0); Conselho Nacional de Desenvolvimento Científico e Tecnológico (404096/2020–4); Coordenação de Aperfeiçoamento de Pessoal de Nível Superior and, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (88887.703169/2022–00). The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. All authors declare no other competing interests.
Author information
Authors and Affiliations
Contributions
ABS and AFMP performed data analysis, intellectual support, writing, and discussion of findings; JAF, AFG, JVF and EHRO performed experiments, data analysis, and technical assistance; ES, SC and JPO contributed with intellectual support; LAF, RCM and JAT performed statistical data analysis and contributed with technical assistance; BFRBS, BEIF and SSF performed data curation; AFMP and VNF gave conception and design of the study and was adviser and supervisor in the whole project.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Santamarina, A.B., de Freitas, J.A., Franco, L.A.M. et al. Nutraceutical blends predict enhanced health via microbiota reshaping improving cytokines and life quality: a Brazilian double-blind randomized trial. Sci Rep 14, 11127 (2024). https://doi.org/10.1038/s41598-024-61909-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-024-61909-3
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.