Metabolic Status Influences Probiotic Efficacy for Depression—PRO-DEMET Randomized Clinical Trial Results

Abstract

Probiotics may represent a safe and easy-to-use treatment option for depression or its metabolic comorbidities. However, it is not known whether metabolic features can influence the efficacy of probiotics treatments for depression. This trial involved a parallel-group, prospective, randomized, double-blind, controlled design. In total, 116 participants with depression received a probiotic preparation containing Lactobacillus helveticus Rosell®-52 and Bifidobacterium longum Rosell®-175 or placebo over 60 days. The psychometric data were assessed longitudinally at five time-points. Data for blood pressure, body weight, waist circumference, complete blood count, serum levels of C-reactive protein, cholesterol, triglycerides, and fasting glucose were measured at the beginning of the intervention period. There was no advantage of probiotics usage over placebo in the depression score overall (PRO vs. PLC: F(1.92) = 0.58; p = 0.45). However, we found a higher rate of minimum clinically important differences in patients supplemented with probiotics than those allocated to placebo generally (74.5 vs. 53.5%; X²(1,n = 94) = 4.53; p = 0.03; NNT = 4.03), as well as in the antidepressant-treated subgroup. Moreover, we found that the more advanced the pre-intervention metabolic abnormalities (such as overweight, excessive central adipose tissue, and liver steatosis), the lower the improvements in psychometric scores. A higher baseline stress level was correlated with better improvements. The current probiotic formulations may only be used as complementary treatments for depressive disorders. Metabolic abnormalities may require more complex treatments. ClinicalTrials.gov identifier: NCT04756544.

Full text

Citation: Gawlik-Kotelnicka, O.;
Margulska, A.; Płeska, K.;
Skowro´nska, A.; Strzelecki, D.
Metabolic Status Influences Probiotic
Efficacy for Depression—PRO-DEMET
Randomized Clinical Trial Results.
Nutrients 2024,16, 1389. https://
doi.org/10.3390/nu16091389
Academic Editor: Qingsen Shang
Received: 8 April 2024
Revised: 29 April 2024
Accepted: 30 April 2024
Published: 3 May 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
nutrients
Article
Metabolic Status Influences Probiotic Efficacy for
Depression—PRO-DEMET Randomized Clinical Trial Results
Oliwia Gawlik-Kotelnicka 1, * , Aleksandra Margulska 2, Kacper Płeska 3, Anna Skowro ´nska 1
and Dominik Strzelecki 1
1Department of Affective and Psychotic Disorders, Medical University of Lodz, Czechoslowacka Street 251,
92-216 Lodz, Poland; anna.zabka@gmail.com (A.S.); dominik.strzelecki@umed.lodz.pl (D.S.)
2Department of Adolescent Psychiatry, Medical University of Lodz, Czechoslowacka Street 8/10,
92-216 Lodz, Poland; aleksandra.margulska@umed.lodz.pl
3Faculty of Medicine, Medical University of Lodz, Kosciuszki Avenue 4, 90-419 Lodz, Poland;
pleskakacper@gmail.com
*Correspondence: oliwia.gawlik@umed.lodz.pl
Abstract: Probiotics may represent a safe and easy-to-use treatment option for depression or its
metabolic comorbidities. However, it is not known whether metabolic features can influence the
efficacy of probiotics treatments for depression. This trial involved a parallel-group, prospective,
randomized, double-blind, controlled design. In total, 116 participants with depression received a
probiotic preparation containing Lactobacillus helveticus Rosell
®
-52 and Bifidobacterium longum Rosell
®
-
175 or placebo over 60 days. The psychometric data were assessed longitudinally at five time-points.
Data for blood pressure, body weight, waist circumference, complete blood count, serum levels of
C-reactive protein, cholesterol, triglycerides, and fasting glucose were measured at the beginning of
the intervention period. There was no advantage of probiotics usage over placebo in the depression
score overall (PRO vs. PLC: F(1.92) = 0.58; p= 0.45). However, we found a higher rate of minimum
clinically important differences in patients supplemented with probiotics than those allocated to
placebo generally (74.5 vs. 53.5%; X
2
(1,n = 94) = 4.53; p= 0.03; NNT = 4.03), as well as in the
antidepressant-treated subgroup. Moreover, we found that the more advanced the pre-intervention
metabolic abnormalities (such as overweight, excessive central adipose tissue, and liver steatosis),
the lower the improvements in psychometric scores. A higher baseline stress level was correlated
with better improvements. The current probiotic formulations may only be used as complementary
treatments for depressive disorders. Metabolic abnormalities may require more complex treatments.
ClinicalTrials.gov identifier: NCT04756544.
Keywords: depression; abdominal obesity; metabolic syndrome; probiotics; anxiety; stress
1. Introduction
Depression is a common illness that affects 280 million people worldwide, with women
experiencing depression at a higher rate than men. It is characterized by the occurrence
of lowered mood levels, decreased interest in daily activities, lack of pleasure, loss of
energy, and decreased thinking ability [
1
]. Metabolic syndrome (MetS), according to the
definition, consists of such disorders as central obesity, dyslipidemia, insulin resistance,
and hypertension [
2
]. A positive correlation between depression and metabolic syndrome
has been repeatedly demonstrated [
3
]. It is estimated that MetS occurs among more than
30% of people suffering from depression and 12.5–31.4% of the general population [4].
Importantly, both obesity and MetS have been found to be independently associated
with depressive symptoms and inflammation. A possible pathophysiological overlap is
being considered, with chronic low-grade inflammation and dysbiosis being suggested as
possible connecting factors [5].
Nutrients 2024,16, 1389. https://doi.org/10.3390/nu16091389 https://www.mdpi.com/journal/nutrients

Nutrients 2024,16, 1389 2 of 21
People who suffer from depression have increased concentrations of inflammatory
state markers, such as interleukin 6 (IL-6), tumor necrosis factor-alpha (TNF-
α
), and other
interleukins, while interferon-gamma (IFN-
γ
) levels are decreased [
6
]. Moreover, the
several pathogenic processes that lead to the development of MetS ultimately result in a
pro-inflammatory state, which explains why people with MetS also have elevated levels of
inflammatory markers, e.g., TNF-α, C-reactive protein (CRP), and IL-6 [7].
The gut microbiota, which consists of approximately 70% Firmicutes and Bacteroides
bacteria, also plays an important role in modulating mental health and central nervous
system function [
8
]. This occurs through the microbiota–gut–brain axis, as a bidirectional
communication network between the gut and brain. Moreover, as human and animal
studies have shown, the composition of the gut microbiota influences the development
of depression and anxiety [
9
]. In addition, dysbiosis can lead to the development of the
components of MetS such as dyslipidemia, obesity, and liver steatosis [10].
The probable common etiopathogenesis of depression and MetS has led to a grow-
ing interest in interventions on the gut microbiota, including the use of probiotics and
prebiotics as supplements that affect the microbiota–gut–brain axis and reduce the risk of
depression [
11
], as well as metabolic syndrome and its sequelae [
12
]. The use of probiotics,
defined as ”microorganisms that, when administered in adequate amounts, confer a health
benefit on the host” [
13
], may have the effect of reducing the intensity of anxiety symp-
toms [
14
]. Moreover, recent studies have reported that probiotics used as complementary
treatments lead to better results in the management of depression [
15
,
16
]. Importantly, a
unique class of probiotics known as psychobiotics may generate or promote the synthesis
of neurotransmitters, SCFAs, enteroendocrine hormones, and anti-inflammatory cytokines.
Psychobiotics may have a wide range of uses, from improved mood and reduced stress
levels to acting as an adjuvant in the therapeutic treatment of several neurodevelopmental
and neurodegenerative illnesses [17–21].
Targeted interventions on the microbiota using probiotics among MetS patients have
indicated propitious effects on obesity, arterial hypertension, glucose metabolism, and
dyslipidemia. Nevertheless, more studies need to be conducted to confirm the positive
impact of probiotics on MetS [22].
Moreover, probiotic supplementation may restore the imbalances in some inflamma-
tory biomarkers or alleviate the clinical signs of chronic inflammation [23].
Therefore, it is important to identify conditions (including clinical characteristics) that
may be supportive of the curative action of probiotics. For instance, there is little but
promising evidence of efficacy of probiotics in reducing the risk of depression or anxiety
during the perinatal period [
24
]. Additionally, probiotics may be beneficial in treating
overweight-related cognitive impairment and anxiety [
25
–
28
]. However, little is known
about whether probiotic mixtures have more favorable effects on psychometric outcomes
in metabolic depression versus depression without metabolic abnormalities [29].
However, the metabolic outcomes with predictive value for the efficacy of probiotics
in treating depression are not known. Specifically, it is not known whether central obesity,
MetS, or its components may be associated with an improvement in depressive symptoms
after microbiota-targeted interventions. Finding such connections may allow personalised
treatments to be optimized.
Based on the above, the PRO-DEMET randomized controlled trial protocol was con-
structed [
30
]. Then, the pilot study was performed with convincing results regarding the
feasibility of a whole-scale study [
31
]. Importantly, several alterations to the study plan
were introduced and explained in the publication of the pilot study results, which are
discussed throughout the current manuscript.
The study’s main aim was to assess the efficacy of probiotics towards depressive,
anxiety, and stress symptoms in patients with depressive disorders stratified by abdominal
obesity or metabolic syndrome comorbidity. The secondary aim was to assess the possible
predictive value of chosen lifestyle, clinical, or laboratory parameters for the efficacy of
probiotics in treating depression.

Nutrients 2024,16, 1389 3 of 21
Our hypothesis was that probiotic supplementation would decrease the level of de-
pressiveness more effectively in metabolic forms of depressive disorders than in depression
without metabolic-associated abnormalities.
This manuscript was planned and prepared according to the CONSORT statement
guidelines [32].
2. Materials and Methods
The PRO-DEMET trial described herein was designed as a single-center, parallel-
group, prospective, randomized, double-blind, placebo-controlled study. It took place at
the Medical University of Lodz (Poland) between December 2020 and May 2023. The study
timeline has been described previously [30] and is shown in Figure 1.
Nutrients 2024, 16, x FOR PEER REVIEW 3 of 26
the possible predictive value of chosen lifestyle, clinical, or laboratory parameters for the
efficacy of probiotics in treating depression.
Our hypothesis was that probiotic supplementation would decrease the level of
depressiveness more effectively in metabolic forms of depressive disorders than in
depression without metabolic-associated abnormalities.
This manuscript was planned and prepared according to the CONSORT statement
guidelines [32].
2. Materials and Methods
The PRO-DEMET trial described herein was designed as a single-center, parallel-
group, prospective, randomized, double-blind, placebo-controlled study. It took place at
the Medical University of Lodz (Poland) between December 2020 and May 2023. The
study timeline has been described previously [30] and is shown in Figure 1.
Figure 1. The study timeline. Abbreviations: MADRS—Montgomery–Asberg Depression Rating
Scale; WC—waist circumference; BP—blood pressure; CBC—complete blood count; CRP—C-
reactive protein; HDL-c—high-density lipoprotein cholesterol; LDL-c—low-density lipoprotein
cholesterol; fGlc—fasting glucose; TG—triglycerides; ALT—alanine aminotransferase; AST—
aspartate aminotransferase; DASS—Depression, Anxiety, Stress Scale; WHOQoLBREF—WHO
Quality of Life BREF Instrument; FFQ—Food Frequency Questionnaire; IPAQ—International
Physical Activity Questionnaire.
2.1. Participants
Adult outpatients (≥18 years) were randomly assigned (1:1) to probiotic (PRO) or
placebo (PLC) groups via computer-generated blocked lists stratified by the presence of
MetS according to the International Diabetes Federation (IDF). Unblinding was
permissible only if any serious adverse events occurred during the trial. Randomization
was performed using a computer-based random number generator
(hps://www.randomizer.org/, accessed on 10 December 2020) operated by an
independent researcher.
Figure 1. The study timeline. Abbreviations: MADRS—Montgomery–Asberg Depression Rating
Scale; WC—waist circumference; BP—blood pressure; CBC—complete blood count;
CRP—C-
reactive
protein; HDL-c—high-density lipoprotein cholesterol; LDL-c—low-density lipoprotein choles-
terol; fGlc—fasting glucose; TG—triglycerides; ALT—alanine aminotransferase; AST—aspartate
aminotransferase; DASS—Depression, Anxiety, Stress Scale; WHOQoLBREF—WHO Quality
of Life BREF Instrument; FFQ—Food Frequency Questionnaire; IPAQ—International Physical
Activity Questionnaire.
2.1. Participants
Adult outpatients (
≥
18 years) were randomly assigned (1:1) to probiotic (PRO) or
placebo (PLC) groups via computer-generated blocked lists stratified by the presence of
MetS according to the International Diabetes Federation (IDF). Unblinding was permissible
only if any serious adverse events occurred during the trial. Randomization was performed
using a computer-based random number generator (
https://www.randomizer.org/
,
accessed on 10 December 2020) operated by an independent researcher.
The study’s entry population finally consisted of 116 patients recruited in primary
care and psychiatric outpatient clinics in central Poland through advertisements in social
media and using the snowball method.
Regarding the sample size, it was assumed to be at least 40 subjects per PRO or PLC
group [
30
]. However, more participants were recruited considering the possible attrition
rate. Due to significant difficulties in enrolling patients with MetS (as reported in the pilot
study [
31
]), we decided to perform a two-arm study controlling for metabolic abnormalities.

Nutrients 2024,16, 1389 4 of 21
Patients with AO constituted about half of the studied population and patients with MetS
about one-fourth.
The first primary inclusion criterion was a diagnosis of depressive disorders according
to the 11th International Classification of Diseases (ICD-11) (depressive episode, recur-
rent depression, mixed depressive and anxiety disorder or dysthymia) [
33
]. The addi-
tional inclusion criterion was a Montgomery–Asberg Depression Rating Scale (MADRS)
score ≥13
based on the clinical utility study by Duarte [
34
]. The exclusion criteria are
listed in Appendix A.
2.2. Interventions
At the beginning of the intervention period, the study subjects were requested not to
make changes in their routine lifestyle activities over the next 60 days. The PRO group re-
ceived one capsule containing the probiotic mixture powder in the amount of 3
×
10
9
colony
forming units (CFU) containing Lactobacillus helveticus Rosell
®
-52, Bifidobacterium longum
Rosell
®
-175, and excipients (Sanprobi Stress
®
, Sanprobi Sp. z o. o., Sp. k., Szczecin, Poland;
probiotic powder manufacturer—Institute Rosell-Lallemand, Montreal, QC, Canada). The
PLC group received the same capsule with only the excipients (Sanprobi Sp. z o. o., Sp. k.,
Szczecin, Poland).
The optimal composition of the probiotic supplement strains, dosage, and intervention
length were selected based on our previous investigation [29].
2.3. Outcome Measures
The outcome measures are shown in Table 1, as explained in the protocol [
30
], the
pilot study manuscript [31], and Appendix B.
Table 1. The PRO-DEMET clinical trial outcome measures.
Outcome Measures
Primary ∆MADRS
Secondary
%∆MADRS, MCID MADRS, CMC MADRS, response MADRS, remission
MADRS, ∆DASS, %∆DASS, ∆D-DASS, %∆D-DASS, ∆A-DASS, %∆A-DASS,
∆S-DASS, %∆S-DASS, MCID DASS, MCID D-DASS, MCID A-DASS, MCID
S-DASS, ∆QoL, %∆QoL, ∆QoLpsy, %∆QoLpsy
Tertiary
(baseline only)
AO presence, MetS presence, weight, WC, WWI, BP, fGlc, HDL-c, non-HDL-c,
TG, ALT, AST, TG/HDL-c, ALT/AST, HSI, MADRS, DASS, QoL, CLGI
presence, CRP, NEU, LYM, MON, PLT, NEU/LYM, MON/LYM, PLT/LYM, SII,
I-FABP, dietary habits, physical activity level, antidepressant treatment
Abbreviations: MCID—minimum clinically important difference; MADRS—Montgomery–Asberg Depres-
sion Rating Scale;
∆
—change between the end (V2) and the beginning (V1) of the intervention pe-
riod; %
∆
—percentage
∆
; CMC—clinically meaningful change; DASS—Depression, Anxiety, and Stress
Scale;
D-DASS
—
Depression–DASS
; A-DASS—Anxiety–DASS; S-DASS—Stress–DASS; QoL—quality of life;
QoLpsy—psychological
QoL;
AO—abdominal
obesity; MetS—metabolic syndrome; WC—waist circumfer-
ence; WWI—Weight-Adjusted Waist Index; BP—blood pressure; fGlc—fasting glucose; HDL-c—high-density
lipoprotein cholesterol; TG—triglycerides; ALT—alanine aminotransferase; AST—aspartate aminotrans-
ferase;
HSI—Hepatic
Steatosis Index; CLGI—chronic low-grade inflammation; CRP—C-reactive protein;
NEU—neutrophils; LYM—lymphocytes; MON—monocytes; PLT—platelets; SII—Systemic Inflammatory Index,
I-FABP—intestinal fatty-acid-binding protein.
2.3.1. Questionnaires and Scales
The characteristics of the questionnaires used may be found in the protocol [30].
Study-specific questionnaires were used to assess demographic, lifestyle, and health-
related data and to gain information on any adverse events or exclusion criteria emerging
during the intervention period.
Validated scales were used to study the patients’ diets (the Food Frequency Question-
naire (FFQ) [
35
]) and assess their symptom severity (the MADRS [
36
]; Depression, Anxiety
and Stress Scale (DASS) [
37
]); and quality of Life (QoL; the WHO Quality of Life BREF
Instrument [38] scores).

Nutrients 2024,16, 1389 5 of 21
The MADRS scoring instructions applied were as follows: 0 to 6 points—the nor-
mal range; 7 to 19 points—mild depression; more than 20 points—at least moderate
depression [36].
2.3.2. Biological Material
The fasting venous blood samples were collected by qualified nurses (9 mL) in the
morning, between 8:00 and 11:00 a.m., after an overnight rest at the beginning (V1) of the
intervention period, and the basic laboratory tests were performed in the Department of
Laboratory Diagnostics, Central Teaching Hospital, Medical University of Lodz, Poland.
2.4. Patient Involvement
The patients were involved in the choice of outcome measures and decisions related
to the management and administration of the trial. We carefully assessed the burden of the
trial interventions on the patients. We have started disseminating the main results to the
trial participants using dedicated website and e-mail messages.
2.5. Data Management
The data were catalogued in compliance with the requirements of findability, accessi-
bility, interoperability, and reusability (FAIR) standards and according to the General Data
Protection Regulation (EU) 2016/679.
2.6. Ethics
The study was conducted in accordance with the Declaration of Helsinki and approved
by the Bioethics Committee of the Medical University of Lodz on 15 December 2020
(reference number RNN/228/20/KE).
2.7. Statistical Methods
The statistical procedures were performed with JASP 0.18.1 (accessed via https://
jasp-stats.org/download/, accessed on 10 February 2024) and STATISTICA 13.1 (TIBCO
Software Inc., Palo Alto, CA, USA). The continuous variables were characterized by means
with standard deviations and the categorical variables by the number of observations with
the proportion (percentage) of the whole. The normality of distribution of continuous
variables was tested with a Shapiro–Wilk test. Accordingly, a U-Mann–Whitney test and
Kruskal–Wallis test were used to test inter-group differences. For the Mann–Whitney test,
the effect size was given by the rank biserial correlation. The associations between variables
were tested using Spearman’s correlation coefficients. A repeated measures ANOVA was
used to verify whether there were statistically significant differences between variables
over time between the probiotic and placebo groups. A multiple linear regression model
and logistic regression analysis were used to evaluate the relationship between various
predictor variables and primary and secondary outcomes. The significance level was set at
p<0.05. As we were facing multiple outcome measures, we chose a single primary outcome
measure, as well as using point estimate and effect size measures wherever possible [
39
,
40
].
3. Results and Discussion
3.1. Study Flowchart
Figure 2shows the CONSORT flow diagram of the study participants.
Regarding the tolerability, no serious adverse events were observed. The adverse
events included an acute upper airway infection (including COVID-19; n = 8), a urinary
tract infection (n = 2), a case of diarrhea (n = 2), headaches (n = 2), exacerbation of an
allergic asthma (n = 1), and a mild nettle-rash (n = 1).
Essentially, the numbers of those who were lost to follow-up or discontinued the
intervention were very similar in the PRO and PLC groups.

Nutrients 2024,16, 1389 6 of 21
Nutrients 2024, 16, x FOR PEER REVIEW 8 of 26
Figure 2 shows the CONSORT flow diagram of the study participants.
Figure 2. Participant flow diagram. MetS: metabolic syndrome; PRO-DM: probiotic + depression +
MetS group; PRO-D: probiotic + depression group; PLC-DM: placebo + depression + MetS group;
PLC-D: placebo + depression group.

Nutrients 2024,16, 1389 7 of 21
Four patients were excluded from the analysis, which happened before the unblinding.
The reasons for definitely feeling better were given spontaneously by the patients them-
selves at the beginning of the V2 meeting, and included a regular psychotherapy routine
being introduced just after the start of the intervention (not reported previously in the
MQ), a national exam being passed to be a specialist in the patient’s occupational field,
a successfully finished divorce trial, and the completion of a medical diagnostic process
that resulted in a significant improvement in physical health. The above were regarded
as major exclusion criteria based on the protocol. However, including all completers did
not change the results of the analysis regarding the MADRS scores (see Supplementary
Information). The analyses were performed as per-protocol analyses. All the patients who
finished the study were compliant, as assessed form the daily medication log. Importantly,
all randomized subjects received the allocated intervention. At the same time, we had a
moderately high rate of non-completers (dropouts; eight in the PRO and nine in the PLC
group). Thus, an intention-to-treat analysis seemed unjustified [
41
]. Nonetheless, this
attrition rate gave the study internal validity [42].
Finally, we analyzed the MADRS scores from 94 participants (one patient was unable to
complete to an in-person or online V2 meeting, meaning the MADRS was impossible to per-
form), the DASS scores from 82 participants, and the QoL scores from 80 participants (some
patients did not give back their self-assessment questionnaires, despite
several reminders
).
3.2. Sample Characteristics
The basic, diet-related, clinical, and laboratory sample characteristics are shown in
Table 2. Importantly, the entry PRO and PLC groups did not differ in terms of their
sociodemographic, general-health-related, or metabolic-health-associated data. The dietary
intakes did not significantly differ between the two groups except for dairy and eggs.
Among the psychometric parameters, only the neurovegetative domain of the MADRS
was higher in the PRO than the PLC group. The lymphocyte (LYM) level was the only
inflammation marker that was lower in the PRO compared with the PLC group. An
apparent lack of virtually any differences between the PLC and PRO groups represented
an obvious strength of our study.
Table 2. Study population characteristics. Data are shown as n (%) or the mean
±
standard deviation.
Total (n = 95) Probiotic (n = 51) Placebo (n = 44) pMissing
Data (%)
Basic characteristics
Sex (F:M) 81:14 (85.3:14.7%) 43:8 (84.3:15.7%) 38:6 (86.4:13.6%) 0.78 0
Age (y) 34.4 ±13.5 34.1 ±12.2 34.6 ±14.7 0.75 0
Ethnicity (%)
Caucasian 95 (100%) 51 (100%) 44 (100%) 0
Diagnosis according to ICD-11
(6A70:6A71:6A73) 8:26:61
(8.4:27.4:64.2%) 7:16:28
(13.7:31.4:54.9%) 1:10:33
(2.3:22.7:75%) 0.06 0
Psychotropic medications 66 (69.5%) 36 (70.6%) 30 (68.2%) 0.80 0
Antidepressants 66 (69.5%) 36 (70.6%) 30 (68.2%) 0.80 0
Antipsychotics 4 (4.2%) 3 (5.9%) 1 (2.3%) 0.38 0
Comorbidities 51 (53.7%) 29 (56.9%) 22 (50.0%) 0.50 0
AO
IDF
Polish 2022 guidelines 54 (56.8%)
34 (35.8%) 28 (54.9%)
17 (33.3%) 26 (59.1%)
17 (38.6%) 0.68
0.59 0
MetS
IDF
Polish 2022 guidelines 23 (24.2%)
24 (25.3%) 11 (21.6%)
12 (23.5%) 12 (27.3%)
12 (27.3%) 0.78
0.68 0
Different than psychotropics
pharmacological treatment 33 (34.7%) 15 (27.3%) 18 (41.2%) 0.16 0
Smoking cigarettes 14 (14.7%) 9 (18.2%) 5 (11.8%) 0.38 0
Dietary supplements 49 (51.6%) 24 (45.5%) 25 (56.9%) 0.27 0
Physical activity [MET-min/week] 1968.91 ±1401.6 2056.72 ±1578.0 1882.10 ±1236.6 0.84 58

Nutrients 2024,16, 1389 8 of 21
Table 2. Cont.
Total (n = 95) Probiotic (n = 51) Placebo (n = 44) pMissing
Data (%)
Dietary habits
Food frequency intake assessed on a scale of 1–6: 1—never or almost never; 2—once a month; 3—several times a month; 4—several
times a week; 5—every day; 6—several times a day.
Sweets and snacks 2.63 ±0.7 2.54 ±0.7 2.74 ±0.7 0.28
2.1
Diary and eggs 3.08 ±0.8 3.96 ±0.7 3.23 ±0.8 0.04 *
Cereal products 3.07 ±0.6 3.03 ±0.5 3.11 ±0.7 0.40
Oils 2.64 ±0.6 2.56 ±0.6 2.73 ±0.7 0.15
Fruits 2.77 ±0.5 2.72 ±0.5 2.83 ±0.6 0.40
Vegetables and seeds 3.36 ±0.6 3.25 ±0.5 3.48 ±0.7 0.10
Meat (including fish) 2.31 ±0.7 2.31 ±0.6 2.31 ±0.9 0.52
Drinks (excluding water) 2.02 ±0.6 2.01 ±0.5 2.04 ±0.6 0.94
Processed food products 2.40 ±0.5 2.34 ±0.4 2.46 ±0.5 0.21
Psychometric data
MADRS score total 20.43 ±5.5 20.94 ±6.1 19.84 ±4.7 0.47 0
MADRS score domains
Sadness
Neurovegetative
Detachment
Negative thoughts
4.42 ±1.7
5.46 ±2.1
7.18 ±2.2
3.21 ±1.4
4.45 ±1.7
5.91 ±2.3
7.11 ±2.4
3.17 ±1.4
4.40 ±1.8
4.96 ±1.8
7.26 ±2.0
3.26 ±1.4
0.97
0.02 *
0.98
0.54
5.3
MADRS score severity
Mild depression
Moderate depression 48 (50.5%)
47 (49.5%) 24 (47.1%)
27 (52.9%) 24 (54.5%)
20 (45.5%) 0.51
0.34 0
DASS score 64.74 ±22.6 63.60 ±22.2 66.07 ±22.0 0.64
2.1
Depression 21.55 ±9.7 20.74 ±10.5 22.49 ±8.9 0.44
Anxiety 17.78 ±8.8 17.84 ±9.0 17.72 ±8.2 0.98
Stress 25.41 ±9.2 25.02 ±8.4 25.86 ±9.4 0.63
QoL score 73.49 ±12.3 74.56 ±12.9 72.26 ±11.6 0.38
Physical 18.84 ±3.9 18.62 ±4.0 19.09 ±4.0 0.43
Psychological 15.41 ±3.6 15.62 ±3.8 15.16 ±3.4 0.63
Social 8.53 ±2.4 8.84 ±2.4 8.16 ±2.6 0.24
Environmental 25.25 ±4.6 25.92 ±4.9 24.47 ±4.0 0.12
Metabolic-health-associated data
Weight (kg) 70.66 ±15.7 69.35 ±15.3 72.17 ±16.2 0.40
0
BMI (kg/m2)24.88 ±4.8 24.29 ±4.1 25.57 ±5.4 0.33
WC (cm) 85.27 ±13.4 84.95 ±12.1 86.80 ±14.9 0.41
WWI (cm/√kg) 10.17 ±0.8 10.12 ±0.8 10.23 ±0.9 0.59
WHtR (cm/cm) 0.51 ±0.1 0.49 ±0.1 0.52 ±0.1 0.41
sBP (mmHg) 121.71 ±14.1 121.90 ±13.9 121.48 ±14.4 0.76
dBP (mmHg) 82.75 ±8.5 82.88 ±8.9 82.59 ±8.1 0.91
fGlc (mmol/L) 5.20 ±0.5 5.17 ±0.5 5.24 ±0.6 0.50
HDL-c (mmol/L) 1.65 ±0.3 1.71 ±0.4 1.58 ±0.3 0.053
non-HDL-c (mmol/L) 3.71 ±1.1 3.76 ±1.1 3.66 ±1.1 0.62
TG (mmol/L) 1.16 ±0.7 1.14 ±0.7 1.18 ±0.6 0.76
TG/HDL-c 0.77 ±0.5 0.73 ±0.5 0.81 ±0.5 0.40
ALT (U/L) 21.61 ±15.2 21.94 ±14.1 21.24 ±16.5 0.37
ALT/AST 0.85 ±0.3 0.86 ±0.4 0.83 ±0.3 0.89
HSI 33.33 ±6.4 32.90 ±6.3 33.82 ±6.7 0.51
Inflammatory data
CRP (mg/L) 2.06 ±2.1 2.10 ±2.0 2.01 ±2.2 0.84
0
CLGI 25 (26.3%) 14 (27.4%) 11 (25%) 0.79
WBC (* 103/)µL6.17 ±1.5 6.05 ±1.5 6.31 ±1.5 0.42
NEU (* 103/)µL3.42 ±1.1 3.39 ±1.2 3.45 ±1.0 0.62

Nutrients 2024,16, 1389 9 of 21
Table 2. Cont.
Total (n = 95) Probiotic (n = 51) Placebo (n = 44) pMissing
Data (%)
MON (* 103/)µL0.51 ±0.2 0.53 ±0.2 0.49 ±0.1 0.31
LYM (* 103/)µL2.01 ±0.5 1.89 ±0.5 2.15 ±0.6 0.02 *
PLT (* 103/)µL280.25 ±55.7 276.69 ±54.6 284.39 ±57.3 0.35
NEU/LYM 1.80 ±0.8 1.89 ±0.9 1.68 ±0.5 0.31
MON/LYM 0.27 ±0.1 0.29 ±0.1 0.24 ±0.1 0.02 *
PLT/LYM 147.14 ±42.8 152.97 ±41.3 140.38 ±44.0 0.04 *
SII 502.89 ±236.7 523.88 ±276.4 478.57 ±180.2 0.64
Others
I-FABP (ng/)mL 1989.4 ±1247.1 2069.2 ±925.3 1894.8 ±1551.7 0.07 1.1
Abbreviations: F—females; M—males; y—years; 6A70—depressive episode; 6A71—recurrent depression;
6A73—mixed depressive and anxiety disorder; MetS—metabolic syndrome; IDF—International Diabetes
Federation; MET—Metabolic Equivalent of Task; MADRS—Montgomery–Asberg Depression Rating Scale;
DASS—Depression
, Anxiety, Stress Scale; QoL—quality of life; BMI—Body Mass Index; WC—waist circum-
ference; WWI—Weight-Adjusted Waist Index; WHtR—Waist to Heigh Ratio; sBP—systolic blood pressure;
dBP—diastolic blood pressure; fGlc—fasting glucose; HDL-c—HDL cholesterol; TG—triglycerides; HSI—Hepatic
Steatosis Index; ALT—alanine aminotransferase; AST—aspartate aminotransferase; CRP—C-reactive protein;
CLGI—chronic low-grade inflammation; WBC—White Blood Cells; NEU—neutrophils; MON—monocytes;
LYM—lymphocytes
; PLT—platelets; SII—Systemic Infalammatory Index; I-FABP—Intestinal Fatty Acid-Binding
Protein; * significant difference between groups.
3.3. Changes in Psychometric Data
Tables 3and 4present a summary of the intervention results measured by the psycho-
metric scales.
Significant but similar improvements in MADRS scores were shown in both the PRO
and PLC groups after the intervention (PRO vs. PLC: F(1.92) = 0.58; p= 0.45). Moreover,
there was no difference in delta MADRS scores (
∆
MADRS; U = 961; Z = 1.02; p= 0.31) nor
in percentage delta MADRS scores (%
∆
MADRS; U = 1003.5; Z = 88; p= 0.38; Figure 3B)
between the PRO and PLC groups (Figure 3A).
Consequently, no differences were observed regarding the
∆
MADRS domain scores
(sadness F(1.73) = 0.42, p= 0.52; neurovegetative F(1.73) = 1.20, p= 0.28; detachment
F(1.73) = 0.56
,p= 0.46; negative thoughts F(1.73) = 0.25, p= 0.62; Figure 3B) nor the
%∆MADRS domain scores between the PRO and PLC groups.
Similarly, the response and remission rates did not differ significantly between the
PRO and PLC groups. Interestingly, the subjects supplemented with PRO showed a higher
rate of MCIDs (n = 38) as compared with the participants supplemented with PLC
(n = 23)
(74.5 vs. 53.5%; X
2
(1,n = 94) = 4.53; p= 0.03; Figure 3C). The effect size, as measured by
Cohen’s d score, was d = 0.45, indicating a medium effect, and the number needed to
treat was NNT = 4.03. The effect of the PRO remained in a subpopulation treated with
antidepressants (n = 66) (75.0 vs. 50.0%; X
2
(1,n = 94) = 4.42; p= 0.04; d = 0.44; NNT = 4.07)
but not in subjects not treated with antidepressants (n = 19) (p= 0.51); in a subpopulation
treated with selective serotonin reuptake inhibitors (SSRIs), similar findings were shown
(70.8 vs. 41.2; p= 0.058) (Figure 3D). Importantly, the antidepressant-treated subjects within
the PRO group had lower basal DASS and D-DASS scores than the participants not treated
with antidepressants (see Supplementary Information).
The total DASS score changes, as well as the depression, anxiety, and stress domain
score changes, were similar in the PRO and PLC groups (F(4.20) = 0.42, p= 0.79). Longitudi-
nal data from DASS measurements at five time-points were additionally assessed, stratified
by an antidepressant treatment, and no differences were shown between the PRO and PLC
groups (Figure 4).

Nutrients 2024,16, 1389 10 of 21
Table 3. Changes in psychometric scale scores between the V2 and V1 time-points. Values show means ±SD.
V1 PRO
(
Mean ±SD
)
V2 PRO
(
Mean ±SD
)
∆PRO
(Mean [95% CI])
%∆PRO
(% [95% CI])
V1 PLC
(Mean ±
SD)
V2 PLC
(Mean ±
SD)
∆PLC
(Mean [95% CI])
%∆PLC
(% [95% CI]) p∆
Difefrence
in ∆PRO–PLC
(Mean [95%CI])
Effect Size r
(Rank Biserial
Correlation)
MADRS score 21.0 ±6.1 16.1 ±6.4 −4.9
[−6.8 to –2.9] −20.98
[−29.7 to −12.3] 19.8 ±4.7 15.9 ±7.8 −3.7
[−6.0 to −1.5] −18.02
[−29.1 to −6.9]
0.31
−1.12
[−4.03, 1.8] 0.124
Sadness 4.45 ±1.7 3.67 ±2.1 −0.87
[−1.6 to −0.1] −9.08
[−28.0 to 9.9] 4.40 ±1.8 3.08 ±2.3 −1.26
[−2.1 to −0.4] −9.52
[−49.9 to 27.9]
0.42
0.35
[−0.74, 1.44] −0.109
Neurovegetative 5.91 ±2.3 4.26 ±2.3 −1.62
[−2.4 to −0.8] −15.22
[−34.3 to 3.9] 4.96 ±1.8 3.97 ±3.0 −1.06
[−2.1 to 0.0] −6.74
[−38.2 to 24.7]
0.19
−0.71
[−2, 0.58] 0.175
Detachment 7.11 ±2.4 5.72 ±2.6 −1.4
[−2.1 to −0.7] −8.29
[−36.7 to 20.1] 7.26 ±2.0 6.14 ±2.7 −1.09
[−2.0 to −0.2] −8.55
[−22.3 to 5.2]
0.26
−0.43
[−1.57, 0.71] 0.153
Negative thoughts 3.17 ±1.4 2.42 ±1.3 −0.82
[−1.3 to −0.3] −17.71
[−32.1 to −3.3.] 3.26 ±1.4 2.44 ±1.4 −0.68
[−1.3 to −0.1] −4.05
[−27.8 to 19.7]
0.68
−0.20
[−0.97, 0.58] 0.055
DASS score 63.6 ±22.2 42.4 ±22.4 −19.9
[−27.1 to −12.6] −25.67
[−40.6 to −10.7] 66.1 ±22.0 43.2 ±27.8 −23.1
[−30.5 to −15.6] −36.53
[−48.0 to −25.1]
0.51
3.17
[−7.11, 13.44] −0.085
Depression 20.7 ±10.5 13.8 ±9.9 −6.3
[−9.0 to −3.5] −20.81
[−47.6 to 6.0] 22.5 ±8.9 15.3 ±11.6 −7.6
[−10.7 to −4.6] −36.65
[−50.1 to −23.2]
0.50
1.39
[−2.59, 5.37] −0.095
Anxiety 17.8 ±9.0 10.3 ±7.2 −6.7
[−9.0 to −4.4] −33.45
[−49.1 to −17.8] 17.7 ±8.2 10.9 ±8.2 −6.6
[−8.9 to −4.5] −40.42
[−52.9 to −27.9]
0.94
−0.04
[−3.22, 3.14] −0.030
Stress 25.0 ±8.4 18.3 ±10.1 −6.6
[−9.6 to −3.0] −19.98
[−34.6 to −5.4] 25.9 ±9.4 17.0 ±11.3 −8.7
[−12.0 to −5.5] −31.50
[−46.5 to −16.5]
0.33
2.45
[−2.16, 7.06] −0.141
QoL score 74.6 ±12.9 81.5 ±13.0 7.4 [3.6 to 11.1]
10.90 [5.0 to 16.8]
72.2 ±11.6 80.2 ±16.6 7.6 [3.8 to 11.5]
10.77 [5.2 to 16.3]
0.93
−0.25
[−5.53, 5.03] −0.012
Psychological 15.6 ±3.8 17.0 ±4.0 1.4 [0.3 to 2.3]
11.50 [4.3 to 18.7]
15.2 ±3.4 17.0 ±4.7 1.9 [0.8 to 2.9]
13.29 [5.8 to 20.8]
0.47
−0.57
[−1.98, 0.84] 0.112
Abbreviations: MADRS—Montgomery–Asberg Depression Rating Scale; DASS—Depression, Anxiety, Stress Scale; QoL—quality of life.

Nutrients 2024,16, 1389 11 of 21
Table 4. Different intervention outcomes according to the MADRS and the DASS.
PRO PLC pOR [95%CI] NNT
MCID MADRS (%) 74.5 53.5 0.03 2.26 [1.05, 5.86] 4
CMC MADRS (%) 41.2 34.9 0.53 1.18 [0.56, 2.96] 16
Response MADRS (%) 15.7 20.9 0.51 0.61 [0.25, 1.98] −19
Remission MADRS (%) 3.9 9.3 0.29 0.37 [0.15, 1.17] −7
MCID DASS (%) 27.3 28.9 0.87 0.85 [0.37, 2.45] −107
MCID D-DASS (%) 22.7 26.3 0.71 0.75 [0.31, 2.29] −34
MCID A-DASS (%) 29.5 26.3 0.74 1.07 [0.46, 3.11] 26
MCID S-DASS (%) 34.1 36.8 0.73 0.88 [0.35, 2.23] −36
Abbreviations: MADRS—Montgomery–Asberg Depression Rating Scale; DASS—Depression, Anxiety, Stress Scale;
MCID—Minimum Clinically Important Difference; CMC—Clinically meaningful Change; D-DASS—Depression-
DASS, A-DASS—Anxiety-DASS; S-DASS—Stress-DASS.
Nutrients 2024, 16, x FOR PEER REVIEW 14 of 26
Figure 3. The influence of probiotic supplementation on MADRS parameter score changes. (A)
%ΔMADRS distribution; (B) MADRS domain scores at V1 and V2 time-points; (C) rates of MADRS
score-specific changes; (D) rates of MCIDs depending on antidepressant treatment. Note: *—p <
0.05. Abbreviations: PRO—probiotic; PLC—placebo; MADRS—Montgomery–Asberg Depression
Rating Scale; V1—the start of the intervention; V2—the end of the intervention; MCID—minimum
clinically significant difference; CMC—clinically meaningful change.
Figure 3. The influence of probiotic supplementation on MADRS parameter score changes.
(A) %∆MADRS
distribution; (B) MADRS domain scores at V1 and V2 time-points; (C) rates
of MADRS score-specific changes; (D) rates of MCIDs depending on antidepressant treatment.
Note:
*p< 0.05
. Abbreviations: PRO—probiotic; PLC—placebo; MADRS—Montgomery–Asberg
Depression Rating Scale; V1—the start of the intervention; V2—the end of the intervention;
MCID—minimum clinically significant difference; CMC—clinically meaningful change.

Nutrients 2024,16, 1389 12 of 21
Nutrients 2024, 16, x FOR PEER REVIEW 15 of 26
Figure 4. The influence of probiotic supplementation on assessments of the DASS score at five time-
points: (A) total sample; (B) antidepressant-treated subjects; (C) subjects not treated with
antidepressants. Abbreviations: DASS—Depression, Anxiety, Stress Scale; V1—the beginning of the
Figure 4. The influence of probiotic supplementation on assessments of the DASS score at five
time-points: (A) total sample; (B) antidepressant-treated subjects; (C) subjects not treated with
antidepressants. Abbreviations: DASS—Depression, Anxiety, Stress Scale; V1—the beginning of
the intervention (0 days); V2—the end of the intervention (60 days); t1—the first monitoring point
(15 days); t2—the second monitoring point (30 days); t3—the third monitoring point (45 days).

Nutrients 2024,16, 1389 13 of 21
Moreover, there were no differences between the PRO and PLC groups for the QoL
score (F(1.78) = 0.01; p= 0.93) or the questionnaire psychological domain score.
A recent meta-analysis of human trials using probiotics demonstrated their possible
usefulness in depressive outcome measures [
43
]. Additionally, probiotics were effective
for patients with both mild and moderate depression. This fact places probiotics next
to nutritional, dietary, and other lifestyle interventions, which may also be effective for
mild depressive symptoms [
44
]. However, our study did not find any significant change
in depression scores overall between the probiotics and placebo groups. We only found
higher rates of MCIDs in subjects supplemented with probiotics than those under placebo
conditions. In most of the research, probiotics were effective in reducing depressive
symptoms only as an add-on treatment [
43
]. We have confirmed those findings, although
again in terms of more frequent minimal differences in depression scores after probiotics
treatments compared with placebo. Importantly, the present study involved an outpatient
clinical population with depression, while most trials before had investigated depressive
symptoms in healthy participants or comorbid or secondary depression patients [
45
].
However, in contrast to most of the meta-analysis findings [
43
], sex and age did not
influence the efficacy of our intervention. Nonetheless, this may have been due to the
insufficient sample size of our single trial. Regarding details of the supplementation
protocol, the results of an umbrella meta-analysis [
45
] suggested administering probiotics
for depressive symptoms for at least 8weeks, which was confirmed to have a minimal effect
by our study results. However, due to between-study heterogeneity, no firm conclusion
could be drawn about the dosages [
45
], although in our study the dose of 3
×
10
9
CFU
was shown to be possibly enough to obtain the minimum clinically significant effect
for depression.
Additionally, the sample size (n = 95) gives our trial better power than all of the previ-
ously published randomized clinical trials performed in clinical populations. Moreover,
our two-strain probiotic composition confirmed the possible utility of the Lactobacillus spp.
and Bifidobacterium spp. combination in clinical populations [
43
]. In detail, we added data
regarding the action of specific probiotic strains (Lactobacillus helveticus Rosell
®
-52 and
Bifidobacterium longum Rosell®-175) towards negative emotional states. In agreement with
our results, no significant difference was found between probiotic and placebo groups
in any psychological outcome measures in participants with low mood levels who were
not currently taking psychotropic medications [
46
]. In the general population, however,
one study found decreases in somatization, depression, and anger–hostility scores [
47
],
although another study revealed no effects of this intervention on wellbeing, quality of
life, emotional regulation, anxiety, mindfulness, and interoceptive awareness [
48
]. Inter-
estingly, altered brain activity was observed in regions implicated in emotional, cognitive,
and face processes in healthy volunteers [
49
]. Similarly to our study results, the probiotic
formulation was shown to be minimally effective as an add-on treatment for depressive
symptoms in the clinical population; interestingly, the improvement was correlated with the
increases in the levels of brain-derived neurotrophic factor and the tryptophan/isoleucine
ratio [
50
,
51
]. Nevertheless, the current findings are contrary to those of previous trials
indicating an overall significant improvement in depressive symptoms in subjects with
subthreshold to moderate depression as a monotherapy; however, the latter intervention
combined the probiotic strains and S-adenosyl methionine and was implemented for a
period of three months [52].
3.4. Pre-Treatment Determinants of Probiotic Efficacy towards Depression
Regarding the hypothesis of the study, there was no difference in
∆
MADRS scores
between the PRO and PLC groups if stratified by the MetS (p= 0.65), HSI > 36 (
p= 0.95
),
or abdominal obesity (p= 0.67) rates. Moreover, the
∆
MADRS scores did not differ be-
tween the PRO and PLC groups when stratified by CLGI presence, sex, antidepressant
treatment, specific psychiatric diagnosis, or comorbidities. Additionally, no regression

Nutrients 2024,16, 1389 14 of 21
model using CLGI presence, sex, antidepressant treatment, specific psychiatric diagnosis,
or comorbidities as variables could explain the ∆MADRS scores.
The response, MCID, CMC, and remission rates were not predicted by age, sex, MetS,
or abdominal obesity presence in the logistic regression models.
The frequency rates of MCID did not differ between the PRO and PLC groups when
stratified by the presence of MetS or CLGI, or the lipid, glycemic, or BP criteria of MetS.
As such, the participants in the PRO group who had achieved an MCID or CMC
were compared.
The MCID achievers in the PRO group (n = 38) were not significantly different from
the non-achievers (n = 13). However, a trend toward statistical significance was shown for
higher consumption rates by achievers compared to non-achievers of unprocessed meat
(2.26
±
0.5 vs. 1.90
±
0.6; p= 0.051), fish (2.43
±
0.7 vs. 1.92
±
0.8; p= 0.071), and drinks
(2.08 ±0.5 vs. 1.80 ±0.3; p= 0.054).
It was found that in the PRO but not the PLC group, the CMC achievers (n = 36;
21 in the
PRO and 15 in the PLC group) compared with the non-achievers (n = 58; 29
in the PRO and 29 in the PLC group) had lower pre-treatment BMI scores (23.17
±
5.1
vs.
25.07 ±3.1
;p= 0.02), lower HSI scores (31.48
±
7.0 vs. 33.90
±
5.6; p= 0.04), higher
MADRS scores (24.29
±
6.3 vs. 18.60
±
4.7; p< 0.001), lower QoL scores (69.86
±
11.5 vs.
77.97 ±12.9
;p= 0.02), and lower QoL psychological scores (14.05
±
3.7 vs. 16.76
±
3.5;
p= 0.02).
In concordance with the above findings, interesting correlations were found between
the %
∆
MADRS, %
∆
DASS, and %
∆
QoL scores and some of the psychometric, metabolic,
and inflammatory data in the PRO group but not in the PLC group (Table 5). Essentially,
the metabolic, psychometric, and inflammatory findings were in the vast majority not
significantly correlated; specifically, the baseline BMI or HSI scores did not correlate with
the baseline MADRS or QoL scores (see Supplementary Information).
Table 5. Correlation heat map between percentage changes (%
∆
) of psychometric parameters and
chosen pre-treatment data in the PRO group.
%∆MADRS %∆DASS %∆D-
DASS
%∆A-
DASS
%∆S-
DASS %∆QoL %∆QoLpsy
BMI
WC
ALT
ALT/AST
HSI
LYM
V1 MADRS
V1 DASS
V1 D-DASS
V1 A-DASS
V1 S-DASS
V1 QoL
V1 OoL
psychological
Nutrients 2024, 16, x FOR PEER REVIEW 17 of 26
the PRO and PLC groups when stratified by CLGI presence, sex, antidepressant treatment,
specific psychiatric diagnosis, or comorbidities. Additionally, no regression model using
CLGI presence, sex, antidepressant treatment, specific psychiatric diagnosis, or
comorbidities as variables could explain the ∆MADRS scores.
The response, MCID, CMC, and remission rates were not predicted by age, sex, MetS,
or abdominal obesity presence in the logistic regression models.
The frequency rates of MCID did not differ between the PRO and PLC groups when
stratified by the presence of MetS or CLGI, or the lipid, glycemic, or BP criteria of MetS.
As such, the participants in the PRO group who had achieved an MCID or CMC were
compared.
The MCID achievers in the PRO group (n = 38) were not significantly different from
the non-achievers (n = 13). However, a trend toward statistical significance was shown for
higher consumption rates by achievers compared to non-achievers of unprocessed meat
(2.26 ± 0.5 vs. 1.90 ± 0.6; p = 0.051), fish (2.43 ± 0.7 vs. 1.92 ± 0.8; p = 0.071), and drinks (2.08
± 0.5 vs. 1.80 ± 0.3; p = 0.054).
It was found that in the PRO but not the PLC group, the CMC achievers (n = 36; 21 in
the PRO and 15 in the PLC group) compared with the non-achievers (n = 58; 29 in the PRO
and 29 in the PLC group) had lower pre-treatment BMI scores (23.17 ± 5.1 vs. 25.07 ± 3.1;
p = 0.02), lower HSI scores (31.48 ± 7.0 vs. 33.90 ± 5.6; p = 0.04), higher MADRS scores (24.29
± 6.3 vs. 18.60 ± 4.7; p < 0.001), lower QoL scores (69.86 ± 11.5 vs. 77.97 ± 12.9; p = 0.02), and
lower QoL psychological scores (14.05 ± 3.7 vs. 16.76 ± 3.5; p = 0.02).
In concordance with the above findings, interesting correlations were found between
the %ΔMADRS, %ΔDASS, and %ΔQoL scores and some of the psychometric, metabolic,
and inflammatory data in the PRO group but not in the PLC group (Table 4). Essentially,
the metabolic, psychometric, and inflammatory findings were in the vast majority not
significantly correlated; specifically, the baseline BMI or HSI scores did not correlate with
the baseline MADRS or QoL scores (see Supplementary Information).
Table 4. Correlation heat map between percentage changes (%Δ) of psychometric parameters and
chosen pre-treatment data in the PRO group.
%ΔMADRS %ΔDASS %ΔD-DASS %ΔA-
DASS
%ΔS-
DASS %ΔQoL %ΔQoLpsy
BMI
WC
ALT
ALT/AST
HSI
LYM
V1 MADRS
V1 DASS
V1 D-DASS
V1 A-DASS
V1 S-DASS
V1 QoL
V1 OoL
psychological
r >0.5 0.4 to 0.5 0.3 to 0.4 0.2 to 0.3 0.1 to 0.2 −0.1 to 0.1 −0.2
to −0.1
−0.2
to −0.3
−0.3
to −0.4 <−0.4
Abbreviations: BMI—Body Mass Index; WC—waist circumference; ALT—alanine
aminotransferase; AST—aspartate aminotransferase; HSI—Hepatic Steatosis Index; V1—the start of
Nutrients 2024, 16, x FOR PEER REVIEW 17 of 26
the PRO and PLC groups when stratified by CLGI presence, sex, antidepressant treatment,
specific psychiatric diagnosis, or comorbidities. Additionally, no regression model using
CLGI presence, sex, antidepressant treatment, specific psychiatric diagnosis, or
comorbidities as variables could explain the ∆MADRS scores.
The response, MCID, CMC, and remission rates were not predicted by age, sex, MetS,
or abdominal obesity presence in the logistic regression models.
The frequency rates of MCID did not differ between the PRO and PLC groups when
stratified by the presence of MetS or CLGI, or the lipid, glycemic, or BP criteria of MetS.
As such, the participants in the PRO group who had achieved an MCID or CMC were
compared.
The MCID achievers in the PRO group (n = 38) were not significantly different from
the non-achievers (n = 13). However, a trend toward statistical significance was shown for
higher consumption rates by achievers compared to non-achievers of unprocessed meat
(2.26 ± 0.5 vs. 1.90 ± 0.6; p = 0.051), fish (2.43 ± 0.7 vs. 1.92 ± 0.8; p = 0.071), and drinks (2.08
± 0.5 vs. 1.80 ± 0.3; p = 0.054).
It was found that in the PRO but not the PLC group, the CMC achievers (n = 36; 21 in
the PRO and 15 in the PLC group) compared with the non-achievers (n = 58; 29 in the PRO
and 29 in the PLC group) had lower pre-treatment BMI scores (23.17 ± 5.1 vs. 25.07 ± 3.1;
p = 0.02), lower HSI scores (31.48 ± 7.0 vs. 33.90 ± 5.6; p = 0.04), higher MADRS scores (24.29
± 6.3 vs. 18.60 ± 4.7; p < 0.001), lower QoL scores (69.86 ± 11.5 vs. 77.97 ± 12.9; p = 0.02), and
lower QoL psychological scores (14.05 ± 3.7 vs. 16.76 ± 3.5; p = 0.02).
In concordance with the above findings, interesting correlations were found between
the %ΔMADRS, %ΔDASS, and %ΔQoL scores and some of the psychometric, metabolic,
and inflammatory data in the PRO group but not in the PLC group (Table 4). Essentially,
the metabolic, psychometric, and inflammatory findings were in the vast majority not
significantly correlated; specifically, the baseline BMI or HSI scores did not correlate with
the baseline MADRS or QoL scores (see Supplementary Information).
Table 4. Correlation heat map between percentage changes (%Δ) of psychometric parameters and
chosen pre-treatment data in the PRO group.
%ΔMADRS %ΔDASS %ΔD-DASS %ΔA-
DASS
%ΔS-
DASS %ΔQoL %ΔQoLpsy
BMI
WC
ALT
ALT/AST
HSI
LYM
V1 MADRS
V1 DASS
V1 D-DASS
V1 A-DASS
V1 S-DASS
V1 QoL
V1 OoL
psychological
r >0.5 0.4 to 0.5 0.3 to 0.4 0.2 to 0.3 0.1 to 0.2 −0.1 to 0.1 −0.2
to −0.1
−0.2
to −0.3
−0.3
to −0.4 <−0.4
Abbreviations: BMI—Body Mass Index; WC—waist circumference; ALT—alanine
aminotransferase; AST—aspartate aminotransferase; HSI—Hepatic Steatosis Index; V1—the start of
Abbreviations: BMI—Body Mass Index; WC—waist circumference; ALT—alanine aminotransferase;
AST—aspartate
aminotransferase; HSI—Hepatic Steatosis Index; V1—the start of the intervention period;
LYM—lymphocytes
; MADRS—Montgomery–Asberg Depression Rating Scale; DASS—Depression, Anxiety,
and Stress Scale; QoL—quality of life; r—a correlation coefficient.
Interestingly, similar associations were shown for the efficacy of antidepressants;
higher immunometabolic depression index scores, including BMI scores, predicted smaller

Nutrients 2024,16, 1389 15 of 21
reductions in depressive symptoms after antidepressant usage but with small effect sizes
and inconsistent associations [
53
]. As antidepressants may act as modulators of gut mi-
crobiota, the underlying mechanisms of the described phenomena may share a common
part [54].
Moreover, a more severe basal A-DASS score was shown to be connected to better
improvements in DASS (U = 42.00; Z = 2.12; p= 0.03) and A-DASS (U = 28.50; Z = 2.88;
p< 0.01) scores in the PRO but not PLC group.
We found that the more advanced the metabolic abnormalities (such as overweight,
excessive central adipose tissue, and liver steatosis), the less evident the improvements
in the psychometric parameters in a self-assessment scale. We hypothesize that more
severe or functionally different forms of dysbiosis connected with higher rates of central
fat storage [
10
] require different or more advanced interventions. These may include
multi-strain probiotic formulations, longer durations of supplementation, or different
strains of probiotics. On the other hand, an individual’s stress level could influence the
self-assessment scale results, as it was significantly correlated with the DASS dimensions
but not most of the MADRS domains (see Supplementary Information).
The above findings are, as far as we know, new to the scientific world, as to the best of
our knowledge metabolic parameters have not been assessed as determinants of the efficacy
of probiotics for depression so far. Few studies have assessed the efficacy of probiotics in
obese patients with depression, and the results are promising for depression but incon-
clusive for obesity [
55
]. Nonetheless, none of the trials compared the psychopathology
outcomes of interventions between obese and lean subjects. We have not found research on
the use of probiotics for depression in patients with comorbid liver steatosis.
Additionally, we found that the stress dimension of psychopathology was the most
positively associated with the efficacy of probiotics for self-assessed anxiety, stress, and
QoL improvements. Importantly, having correlated psychopathological pre-intervention
data, we have found that the S-DASS was the only outlier (see Supplementary Information).
In line with this finding, another study found that the use of probiotics could reduce the
subjective stress levels of healthy participants and improve their stress-related subclinical
anxiety or depression symptoms [
56
]. Additionally, strain-dependent effects on outcomes
related specifically to stress were found in animal studies [57].
Furthermore, a higher LYM level was positively correlated with an improvement in
QoL after treatment with probiotics. A lower LYM level may be a result of chronic stress
of a different origin (hypercortisolemia) [
58
], and the data on the actions of probiotics in
different baseline cortisol conditions remains inconclusive [
56
,
59
], with the topic requiring
further investigation.
As the majority of the gut microbiota is thought to be influenced by diet [
60
], we
hypothesized that the diet’s composition would influence the efficacy of the probiotics.
Surprisingly, it was shown to be non-significant. The findings may be explained by the
fact that we assessed only dietary habits and not anti-inflammatory or microbiota-affecting
indices. However, a trend toward significance was shown for higher consumption rates
of unprocessed meat, fish, and drinks, including juices, in participants who had achieved
an MCID. In line with this finding, a recent meta-review supported the evidence for the
relevance of diet and other lifestyle habits in psychiatric treatments [61]. This may be due
to anti-inflammatory, antioxidative, or microbiota-modulating actions [
62
]. The physical
activity level was also shown to be non-significant. Nonetheless, it was the only index
with a large amount of missing data. Overall, significant interactions between healthy
behaviours and probiotic positive effects on anxiety and emotional regulation were shown
by another study [48].
Romijn et al. revealed that the baseline vitamin D level influenced the treatment
effect of probiotics [
46
]. We did not measure the level of vitamin D; however, we gathered
information on vitamin D supplementation. There was no influence of this supplementation
on the intervention outcome measures in our trial.

Nutrients 2024,16, 1389 16 of 21
Mood disorders were shown to be connected with increased gut permeability [
63
].
In our previous study, a statistically significant positive correlation between I-FABP and
anxiety levels was found (in review). However, in the present study, I-FABP was not
connected with the efficacy of probiotics for any of the dimensions of negative affective
states. This may have been because I-FABP is a marker of increased intestinal permeability
only when enterocyte microdamage occurs [63].
Finally, our findings have indicated that probiotic supplementation is safe and
well-tolerated.
4. Strengths and Limitations
We used a diverse range of outcome measures and both professional-assessed and self-
assessed psychometric scales. Some of the outcomes were new in the field, e.g., the WWI,
WHtR, and inflammatory markers calculated from CBC findings, such as the MON/LYM
ratio. Moreover, we used quite restrictive exclusion criteria and controlled for known
confounding factors affecting microbiota, e.g., diet or physical activity.
Our study had several limitations. The sample size was small or modest; however,
to the best of our knowledge, none of the previously published trials on probiotics in
depression exceeded that number of participants. We did not confirm diagnoses of fatty
liver nor measured percentages of body fat. We did not obtain data on gastrointestinal
symptoms either. More advanced indicators of inflammation or dysbiosis should probably
have been used, such as Il-6 or the gut microbiota composition. Further, we possibly should
not have excluded all of the confounders, e.g., unrecognized chronic inflammatory diseases,
hormonal contraceptive use, or menstrual phase.
Finally, it is worth noting that the variance in intervention outcomes may be explained
by non-specific factors. The significant expectancy effect, with a large effect size, may have
played a huge role in the current study.
5. Conclusions
To conclude, currently probiotics formulations may only be used as a complementary
treatment for depressive disorders. Importantly, comorbid obesity or liver steatosis may
influence the efficacy of probiotics treatments for depression, anxiety, and stress. However,
further research on the details of such interventions is essential.
Supplementary Materials: The following supporting information can be downloaded at: https://
www.mdpi.com/article/10.3390/nu16091389/s1, Supplementary S1: Basal psychopathology scores
in the PRO group depend on antidepressant treatment; Supplementary S2: Basal MADRS and DASS
score, or antidepressant use in the PRO group depending on abdominal obesity or HSI > 36 presence;
Supplementary S3: Correlation analysis of basal psychometric and metabolic data;
Supplementary S4
:
Correlation analysis of basal psychopathological data.
Author Contributions: O.G.-K.—Conceptualization, methodology, software, validation, investiga-
tion, data curation, writing—original draft, visualization, project administration. A.M.—Methodology,
formal analysis, writing—original draft. K.P.—Writing—original draft, visualization. A.S.—Investigation.
D.S.—Resources, supervision, funding acquisition. All authors have read and agreed to the published
version of the manuscript.
Funding: This research was funded by the Medical University of Lodz, grant numbers 503/1-155-
02/503-11-003-20 and 502-03/1-155-02/502-14-386-18.
Institutional Review Board Statement: The study was conducted according to the guidelines of the
Declaration of Helsinki and approved by the Institutional Ethics Committee of the Medical University
of Lodz (15 December 2020; reference number RNN/228/20/KE).
Informed Consent Statement: Informed consent was obtained from all subjects involved in
the study
.
Data Availability Statement: Data will be made available on request.
Conflicts of Interest: The authors have no competing interests to declare.

Nutrients 2024,16, 1389 17 of 21
Appendix A
Exclusion Criteria
The exclusion criteria were as follows: a change in antidepressant or antianxiety med-
ications three weeks prior to the beginning of the study; pregnancy; a potential change
regarding the intestinal microbiota in the previous four weeks, e.g., an infection, vaccina-
tion, or treatment with antibiotics; supplementation with probiotics or prebiotics; being
diagnosed with or having new symptoms of autoimmune disorders, being seriously im-
munocompromised, inflammatory bowel diseases, cancer, or an IgE-dependent allergy; a
significant change in a dietary pattern or a dietary supplement; a significant change in daily
physical activity levels or an extreme sport activity; a significant change in smoking pattern;
a significant change in the treatment schema with proton pump inhibitors, metformin,
laxatives, systemic steroids, nonsteroidal anti-inflammatory drugs, antipsychotics, or any
other medications influencing the microbiota according to the current knowledge; current
decompensated serious somatic disease; psychiatric comorbidities (except for a specific
personality disorder, an additional specific anxiety disorder, and caffeine or nicotine addic-
tion); a major neurological disorder or any medical disability that may have interfered with
a subject’s ability to complete the study procedures; a high risk of suicide; current or recent
participation in another research study involving an intervention that may have altered
outcomes relevant for this study.
Appendix B
Outcome Measures
Delta (∆) was defined as the post- (V2) minus pre-intervention (V1) score difference.
Here, %delta (%∆) was defined as the ∆score/V1 score ratio multiplied by 100%.
Regarding the Montgomery–Asberg Depression Rating Scale (MADRS), a minimum
clinically important difference (MCID) was defined as an improvement of at least two
points [
64
] and a clinically meaningful change (CMC) as an improvement of at least six
points [
65
]. Response to treatment was defined as a decrease in the initial score of at
least 50% [
66
]. An MADRS score of <5 was chosen for narrowly defined remission [
67
].
A four-factor
model of the MADRS was applied and included sadness, a neurovegetative
state, detachment, and negative thoughts [68].
Regarding the Depression, Anxiety, and Stress Scale (DASS), an MCID was defined as
an improvement of six points for every subscale and consequently of eighteen points for
the whole scale based upon a clinical outpatient population calculation [69].
Abdominal obesity (AO) and metabolic syndrome (MetS) were diagnosed based on
the International Diabetes Federation (IDF) criteria [
70
] and simultaneously the Polish
Guidelines [71].
The Body Mass Index was calculated as weight/height
2
ratio and expressed in kg/m
2
,
with values ≥25 considered overweight and ≥30 as obese.
The waist circumference (WC) was measured on the midaxillary line between the
lowest border of the rib cage and the top of the iliac crest.
The Weight-Adjusted Waist index (WWI), calculated as WC/
√
weight, serves as a new
obesity index, surpassing the BMI and WC in evaluating lean and fat masses [72].
The waist-to-height ratio (WHtR) was shown to be superior over the WC and BMI for
detecting cardiometabolic risk factors in both sexes [73].
The triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-c) has been
proposed as a biomarker of insulin resistance and MetS development [74].
The alanine transaminase-to-aspartate transaminase ratio (ALT/AST) can be associ-
ated with excessive fat storage in hepatocytes, and a cut-off of 1.33 was found to provide
predictive value for detecting hepatic steatosis [75].
A Hepatic Steatosis Index (HSI; 8
×
ALT/AST + BMI (+2 with type 2 diabetes mellitus,
+2 if female)) score at cut-off >36 may predict steatosis liver disease [76].

Nutrients 2024,16, 1389 18 of 21
The neutrophil-to-lymphocyte ratio (NEU/LYM) is used to quantify systemic inflam-
mation. It has a typical range of 1–2; values more than 3.0 and lower than 0.7 are pathologi-
cal, and the range of 2.3–3.0 may be a sign of a chronic inflammatory pathology [77].
The reference range of the platelet-to-lymphocyte ratio (PLT/LYM) is 75–199, and an
increase is an indicator of inflammation [78].
The monocyte-to-lymphocyte ratio (MON/LYM) inflammatory marker reference range
is 0.39–0.58 [79].
A high Systemic Immune Inflammation Index (SII; NEU*PLT/LYM) level is defined
as more than 600
×
10
9
cells/L and can serve as a potential predictive factor for systemic
inflammation [80].
Increased levels of intestinal fatty-acid-binding protein (I-FABP) occur in cases of
intestinal epithelial cell damage; thus, it is estimated to be a marker of “leaky gut”.
Chronic low-grade inflammation (CLGI) was defined as serum C-reactive protein
(CRP) levels >3 mg/L [81].
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