1. Introduction
The gut microbiota is the largest microbial community in humans and its importance for human health is hard to estimate. Observational studies have observed its influences on human metabolic health, and different comparative surveys have demonstrated associations between metabolic disorders such as obesity, cardiovascular disease, and type 2 diabetes and the underrepresentation of certain commensal microbial taxa as well as the increased prevalence of potential pathobionts [
1]. Gut microbiota mainly include prokaryotic species (bacteria) that can be taxonomically classified into kingdoms, phyla, classes, orders, families, genera, and species [
2]. The phyla Bacteroidota and Firmicutes represent 90% of total gut microbiota, but other phyla, such as Actinobacteria, Proteobacteria, Fusobacteria, and Verrucomicrobia, are also frequently present [
3].
A growing body of evidence accumulated by studies of gut microbiota in world populations emphasizes that lifestyle, and especially diet, strongly impacts microbiota composition and, thus, human health. In addition to most common omnivorous (O) diet, dietary patterns such as vegan (V), vegetarian (VE), and low-carbohydrate, high-fat (LCHF) diet have become popular recently [
4]. It has been shown that the gut microbiota of adults that consume more animal protein is dominated by
Bacteroides, whereas
Prevotella is associated with a plant-based diet [
2]. A high intake of saturated fatty acids (SFA), sugar, and salt promote the growth of pathogenic bacteria, and, contrarily, the intake of dietary fiber and plant protein increases the abundance of beneficial bacteria that promote the production of short-chain fatty acids (SCFAs) [
5].
Despite the known effects of diet on shaping gut microbiota composition, not many studies have systematically evaluated the associations between dietary patterns and gut microbiota. In studies comparing O, VE, and V, a lower relative abundance of
Bacteroides in V and VE [
6], and a greater diversity of microbiota in V, compared to O, have been observed [
7]. Additionally, a higher relative abundance of bacteria from the phylum Actinobacteria, a lower abundance of bacteria from the phylum Proteobacteria, and a higher ratio between the genera
Prevotella and
Bacteroides in VE, compared to O, have been reported [
8]. The phylum Bacteroidota was dominant among all three diet groups, and a statistically significant difference in the abundance of Bacteroidota was observed between V and O, and VE and O [
7].
On the contrary, a systematic review of the literature found no associations between V or VE diets and microbiota composition compared to O. Some studies show conflicting results, which could be due to differences between individuals and different methods used [
9]. An LCHF diet has been associated with a lower relative abundance of
Bifidobacteria and a higher abundance of
Akkermansia and
E. coli [
5], but no studies have compared it to other dietary patterns. It seems that, more than by dietary pattern, gut microbiota could be shaped by the intake of specific nutrients [
7]. Moreover, due to the immense variability in microbial composition at the species level, it is still not known what may constitute the elusive “golden standard” of a healthy gut microbiota [
10].
The aim of the present study was to determine whether a long-term dietary pattern can impact the composition of gut microbiota at the genus level. In particular, we were interested in whether adherence to a particular dietary pattern alters the microbiota to such an extent that it is possible to determine which pattern a person adheres to based on their gut microbiota composition. For that purpose, dietary data were collected from a cohort of subjects adhering to O, VE, V, and LCHF diet that were equally distributed between groups and were homogenous by age, gender, and BMI. K-means clustering of the microbiota dataset at the genus level was performed, and the nearest neighbor classifier based on approximately two hundred variables was applied to predict the clustering classes of gut microbiota.
4. Discussion
To investigate the relationship between distinct dietary patterns and gut microbiota composition, a cross-sectional study in subjects adhering to omnivorous (O), vegetarian (VE), vegan (V), and low-carbohydrate, high-fat (LCHF) diet was performed. The subjects were equally distributed between groups, were from the same geographical location, and did not differ in age, gender, anthropometric measurements, education, or socioeconomic status, which are factors that can significantly influence gut microbiota composition [
28]. A statistically significant relationship was observed between dietary pattern and the type of environment growing up and growing up with pets, as the majority of V and the minority of O grew up in a rural environment with pets. Similarly, a recent study showed that individuals who grew up around a variety of pets were more likely to engage in greater levels of veganism [
29]. Living in a rural environment could influence environmental consciousness, and it has been observed that the progression from O diet to VE and V diets is associated with increased environmental sustainability [
30].
Despite no major differences in lifestyle factors, we looked for any differences in serum biomarkers between the four groups. We were especially interested in lipid profile and inflammatory status, factors that have been favorably associated with plant-based diets [
31]. Statistically significant differences between the groups were observed for serum cholesterol, LDL, and HDL levels that were the highest in LCHF and lowest in V. The same increase in LDL after adhering to an LCHF diet was observed in other studies, at least in short-term studies [
32,
33,
34], whereas long-term studies are lacking. However, a meta-analysis showed no significant differences in LDL after 6, 12, and 24 months of an LCHF diet [
35]. Nevertheless, it is important to interpret these results with caution, as the majority of studies use LCHF diets as a weight-loss tool in subjects with obesity, whereas the subjects in our study had a normal body mass which was stable and were not pursuing weight loss.
In addition to serum biomarkers, many differences in dietary intake between the groups were observed. The consolidation of dietary intake into principle components revealed clear separation between the groups. VE was a more heterogenous group compared to others, as some only exclude meat from the diet, whereas others also exclude fish, dairy, or eggs [
36]. As expected, the four diet groups differed significantly in the intake of all macronutrients. Total and animal protein intake was the highest in LCHF, which is typical for an LCHF diet [
37], and lowest in V, and similar observations were made in studies that compared V with VE and O [
38,
39]. As expected, the intake of fats, SFA, MUFA, and cholesterol was the highest in LCHF and lowest in V, and the contrary occurred for the intake of plant protein, carbohydrates, and dietary fiber. Only V and VE reached the RDI for dietary fiber, similarly to a recent systematic review [
40]. The intake of free sugars was the lowest in LCHF, which is typical for an LCHF diet [
41]. Many differences in the intake of micronutrients were observed between groups (summed from diet and dietary supplements). V had the highest intakes of α- and β-carotene, vitamin K, copper, vitamin E, vitamin C, folate, and also vitamin B12, due to dietary supplements. Similarly, a higher intake of folate and vitamins C and E was observed in plant-based diets compared to meat-eaters [
40]. On the other hand, LCHF had the highest intake of biotin, pantothenic acid, manganese, selenium, and riboflavin. Additionally, we observed a higher adherence to Mediterranean diet in V and VE, and the same was reported in other research [
39,
42].
Our primary research focus was gut microbiota composition, and we were also interested in differences in stool consistency and GI symptoms between the groups. LCHF reported having fewer GI symptoms, especially flatulence, which was the highest in V. V also had the loosest stools, which was already shown in previous research that observed that consuming more dietary fiber was associated with softer stools [
43]. It has been known for a long time that Bacteroidota and Firmicutes are the predominant phyla and represent more than 90% of the whole gut microbiota [
7], and we observed the same in all four diet groups. Significant differences in the relative abundance of Actinobacteria, Desulfobacterota, and Verrucomicrobiota were observed between groups. LCHF had the lowest abundance of Actinobacteria and the highest abundance of Desulfobacterota. Similarly, a lower abundance of Actinobacteria was observed in children after a 6-month ketogenic diet [
44]; however, long-term studies are lacking. O had the highest abundance of Verrucomicrobiota, whereas V had the lowest. The four predominant genera in all diet groups were
Bacteroides,
Faecalibacterium,
Prevotella 9, and
Alistipes, and many significant differences were observed between groups. In V, the predominant genera were
Prevotella 9,
Bifidobacterium,
Haemophilus,
Lachnospiraceae UCG-004,
Subdoligranulum, and
Anaerostipes. Similar observations about the association of the genus
Prevotella with a high intake of carbohydrates, which is typical for a V diet, were already made in previous research [
45]. Plant foods high in polyphenols, frequently consumed in V, have been associated with a higher abundance of
Bifidobacterium [
46], and a higher abundance of
Subdoligranulum in V and VE compared to O was observed previously in the Slovenian population [
47]. In VE, the predominant genera were
Ruminococcaceae CAG-352,
Lachnospiraceae UCG-001, and
Oscillospiraceae UCG-003. In O, the predominant genera were
Agathobacter,
Lachnospiraceae ND3007,
Victivallis, Ruminococcus,
Rhodospirillales uncultured,
Blautia, and
Izemoplasmatales. One study observed a higher abundance of
Blautia in O compared to V [
48]. In LCHF, the predominant genera were
Alistipes,
Ruminococcus torques, Lachnospiraceae uncultured,
Odoribacter,
Butyricimonas,
Ruminococcaceae uncultured,
Fusicatenibacter,
Desulfovibrio, and
Anaerosporobacter. Similarly, an increase in the abundance of
Alistipes,
Odoribacter,
Butyricimonas, and
Desulfovibrio and a decrease in the abundance of
Bifidobacterium was observed in overweight adults after a 4-week LCHF diet designed for weight loss [
49]; however, long-term studies in adults with a normal BMI are lacking.
Additionally, we focused on the relationship between dietary intake and gut microbiota composition. The intake of dietary fiber, carbohydrates, and plant protein was positively correlated with
Lachnospiraceae UCG-004 and
Haemophilus, and the intake of carbohydrates and plant protein was also positively correlated with
Agathobacter,
Bifidobacterium, and
Anaerostipes. Similar to our study, a study in adult men observed an association between dietary fiber intake and
Haemophilus and
Bifidobacterium [
50], and it is clear that
Bifidobacterium are able to utilize a diverse range of dietary carbohydrates [
51]. Several species of Lachnospiraceae were also associated with dietary fiber and plant protein intake in previous research [
52]. In the present study, the intake of fats, SFA, and animal protein was positively correlated with
Ruminococcaceae uncultured,
Ruminococcus torques,
Anaerosporobacter, and
Odoribacter, and the intake of animal protein was also positively correlated with
Butyricimonas,
Lachnospiraceae uncultured,
Barnesiellaceae uncultured,
Rhodospirillales uncultured, and
Alistipes. Similarly, a higher abundance of
Ruminococcaceae uncultured was observed in subjects with high SFA intake [
53]. A higher abundance of
Odoribacter was observed in mice fed a diet rich in animal protein [
54], and
Alistipes in humans consuming an animal-based diet [
55].
After determining the differences between the four diet groups, we were interested in whether gut microbiota at the genus level could be a useful indicator of a long-term dietary pattern. Hierarchical clustering revealed that subjects can be classified in four clusters depending on gut microbiota composition; C1 was most abundant in
Alistipes, Roseburia, Agathobacter,
Lachnospiraceae uncultured, and
Barnesiella; C2 in
Prevotella 9,
Lachnospira,
Phascolarctobacterium, and
Anaerostipes; C3 in
Faecalibacterium,
Lachnospiraceae NK4A136,
Clostridia vadinBB60,
Bacilli RF39,
Christensenellaceae R-7, and
Clostridia UCG-014; and C4 in
Bacteroides,
Parasutterella, and
Monoglobus. C2 constituted only of V, whereas other clusters were mixed depending on the dietary pattern. Thus, we can conclude that gut microbiota composition at the genus level is not a useful indicator of a subject’s dietary pattern, with the exception of a high abundance of the genus
Prevotella 9, which indicates a V diet. However, it is important to note that an individual could be following a V diet and not have this specific gut microbiota composition, as V were also classified in C1, C3, and C4. Most subjects following an LCHF diet were classified in C1, which is characterized by a high abundance of Proteobacteria and
Alistipes. Indeed, diets with a low intake of fiber and a high intake of fats have been shown to increase the abundance of
Alistipes [
55,
56], and intake of dietary cholesterol was shown to correlate with Proteobacteria [
57]. Most O were classified in C3 and C4, whereas VE was the most heterogenous group and was almost equally classified in C1, C3, and C4.
After this observation, we built a model to explain which lifestyle factors can predict specific gut microbiota composition regardless of dietary pattern. The limitation of the present study is a relatively small sample size. In order to have the groups of subjects adhering to four different dietary patterns homogenous by age, gender, and BMI, we included a sample size of 89 subjects. Subjects adhering to LCHF were particularly hard to recruit, as they needed to be adhering to an LCHF diet for a minimum of 6 months while also having a suitable BMI and keeping a stable body mass for at least 3 months. All of these criteria substantially limited our sample size. Because of the small sample size, the hold-out method and also 10-fold cross-validation were not the right choice to validate our model. Instead, we used the leave-one-out method, which is appropriate for small datasets. A larger dataset would also allow us to perform nested cross-validation and thus optimize the hyperparameters independently, e.g., the similarity distance and the number of nearest neighbors in the case of the k-nearest neighbor classifier and the final set of variables. The choice of these parameters is biased to some extent, since they were not optimized by nested cross-validation, which could lead to an overly optimistic result.
Among anthropometric measurements, significant predictors of gut microbiota composition were hip circumference, phase angle, and diastolic blood pressure. Subjects in C1 had the highest hip circumference, and C4 the lowest, whereas phase angle was the highest in C2. Similarly, it has been observed that anthropometric measurements such as BMI, mid-upper arm, and waist circumference, and waist-to-hip ratio were significantly associated with lower α-diversity and changes in gut microbiota composition [
58]. Additionally, one study identified measures of obesity (waist-to-hip ratio, BMI, visceral fat index) as significant gut microbiota composition predictors in healthy adults [
59]. It seems that the gut microbiota also plays an important role in the development and pathogenesis of hypertension [
60], as hypertension and systolic blood pressure have been inversely associated with α-diversity of gut microbiota [
61]. In the present study, subjects in C2, where
Prevotella 9 was a predominant genus, had the most favorable anthropometric measurements.
Significant gut microbiota composition predictors from the group of serum biomarkers were serum levels of TAG and LBP. Similarly, it has been observed that gut microbiota is associated with blood lipids metabolism in healthy adults, independent of age, gender, and genetics [
62]. LBP, which is highly correlated with lipopolysaccharide (LPS) levels, has been recognized as a reliable systemic biomarker of intestinal permeability, especially in healthy adults who generally have low concentrations of LPS [
63]. Only one study in healthy premenopausal women observed an association between LBP levels and changes in diversity and gut microbiota composition, especially with bacteria that were previously associated with obesity and inflammation, such as
Bacteroides [
64]. In our study, subjects in C1, with a high abundance of Proteobacteria and
Alistipes, had a worse metabolic profile compared to other clusters, with higher levels of TAG and LBP. Similarly,
Alistipes has been implicated to play a critical role in inflammation and disease [
65], and the same is true for Proteobacteria [
66]. Higher abundances of
Alistipes have also been associated with TAG in children [
67].
Many lifestyle factors were identified as significant gut microbiota composition predictors, such as growing up with pets, currently having pets, smoking, sleeping more on weekends, work schedule, last use of antibiotics, family history of dementia, and having alive parents. C2 was the most distinct group, and was the only group where all subjects grew up with pets and had both parents alive, and was also the group where family history of dementia was the most prevalent. On the other hand, in C3, current pet ownership was the most prevalent and the family history of dementia the least prevalent among all clusters. The gut microbiota has been proposed as a determinant of healthy aging, as a higher prevalence of health-associated bacteria, such as
Bifidobacterium and
Christensenellaceae, has been associated with longevity [
68]. The association between a family history of dementia, which is commonly associated with aging, and gut microbiota has not been described in studies, whereas patients with Alzheimer’s disease spectrum, including mild cognitive impairment, have reduced gut microbiota diversity and altered gut microbiota composition [
69]. Regarding pets, numerous studies have already observed that early-life exposure to household pets [
70] and current pet ownership are associated with changes in the human gut microbiota [
71,
72,
73]. C2 had the most flexible work schedule, and only the minority of them were sleeping more on weekends, whereas in C3, the vast majority of subjects were working one shift, which is the most common work schedule in our society. A few studies highlighted the importance of circadian clocks for gut microbiota composition and function [
74], and observed that night work alters gut microbiota composition [
75]. For smoking, a systematic review observed a reduction in bacterial species diversity in smokers. Interestingly, the abundance of
Prevotella was significantly increased in smokers, and the same was observed in the phylum Proteobacteria [
76], while the abundance of
Faecalibacterium was significantly lower in smokers [
77]. Similarly, in our study, we observed that smoking was the most prevalent in C2, which had the highest abundance of
Prevotella 9, and least present in C3, which had the highest abundance of
Faecalibacterium. Regarding the use of antibiotics, which has been identified as a significant predictor, none of the subjects in C2 used antibiotics 3 to 5 months prior to their participation in the study. It has been clear for a long time that antibiotics induce changes in the composition and diversity of gut microbiota; however, after stopping their use, the gut microbiota returns to baseline within a few weeks [
78].
The intensity of GI symptoms and the regularity of bowel movements were also identified as significant gut microbiota composition predictors. It has been observed that gut microbiota dysbiosis may contribute to irregular bowel movement and functional constipation [
79]. The gut bacteria ferment nondigestible carbohydrates, produce flatulence, and can aggravate some GI symptoms [
80]. Additionally, patients with flatulence and borborygmi have a poor tolerance of intestinal gas, which has been associated with gut microbiota instability [
81].
Among psychological factors, significant gut microbiota composition predictors were subjective general health and mood, and symptoms of depression. Similar to our study, one of the most important factors that have been associated with gut microbiota is subjective mood, even in adults without mood disorders [
82]. Numerous studies have observed an association between depression and gut microbiota composition, such as a higher abundance of proinflammatory species and a lower abundance of bacteria that produce SCFA [
83,
84,
85]. In the present study, symptoms of depression were the least prevalent in C3, which has been characterized by a high abundance of
Faecalibacterium that has been reported to improve depressive behavior. Lower abundances of
Faecalibacterium have been observed in patients with depression [
86], and its abundance has been positively associated with quality of life [
87].
Significant gut microbiota composition predictors from the category of specific nutrients intake were the intake of SFA, sugars, free sugars, magnesium, iodine, and manganese. In mice, it has been observed that manganese is vital for proper maintenance of the intestinal barrier [
88], but human studies are lacking. Most studies about the relationship between magnesium and gut microbiota have also been performed on animals; however, one study observed that magnesium supplements can modulate gut microbiota composition and the gut–brain axis in adults with GI functional disorders [
89]. Subjects in C2, which was the group that most differed from all others, had the lowest intake of SFA and iodine. Similarly, it has been suggested that gut microbiota may play a role in the absorption of iodine, and the intake of iodine could have an important impact on gut microbiota [
90]. Subjects in C4 had the highest intake of SFA and free sugars and the lowest intake of manganese. A systematic review observed that a high intake of SFA may negatively affect gut microbiota richness and diversity [
91], whereas a high sugar intake can disrupt gut microbiota stability with a higher abundance of Proteobacteria, increased proinflammatory properties, and a decreased capacity to regulate epithelial integrity [
92].
Overall, our findings suggest that lifestyle factors in combination with the intake of specific nutrients are more important predictors than just dietary pattern alone. Based on our model, 26 variables were crucial to very accurately (in 91%) predict in which cluster an individual’s microbiota was classified. Subjects’ microbiota composition can be classified in specific clusters not only depending on their nutrient intake, but also depending on their anthropometric measurements, the environment in which they live, living with pets, work schedule, family history, and psychological and other lifestyle factors. These factors can be causally, consequentially, or bidirectionally linked to gut microbiota composition. Some of the factors can be modified with changes in lifestyle, such as changes in the intake of specific nutrients or anthropometric measurements, while some, such as family history of dementia or the living environment, are nonmodifiable factors. This should be taken into account when developing strategies aiming to modulate gut microbiota composition.