Distinct microbial taxa are associated with LDL-cholesterol reduction after 12 weeks of Lactobacillus plantarum intake in mild hypercholesterolemia

Distinct microbial taxa are associated with LDL-cholesterol reduction after 12 weeks of Lactobacillus plantarum intake in mild hypercholesterolemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distinct microbial taxa are associated with LDL-cholesterol reduction after 12 weeks of Lactobacillus plantarum intake in mild hypercholesterolemia Felix Kerlikowsky, Mattea Müller, Theresa Greupner, Lena Amend, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2892874/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2023 Read the published version in Probiotics and Antimicrobial Proteins → Version 1 posted 9 You are reading this latest preprint version Abstract Probiotic microbes such as Lactobacillus may reduce serum total cholesterol (TC) and low-density lipoprotein (LDL) cholesterol. The objective of this study was to assess the effect of Lactobacillus plantarum strains CECT7527, CECT7528 and CECT7529 (LP) on the serum lipids, cardiovascular parameters and fecal gut microbiota composition in patients with mild hypercholesterolemia. A randomized, double-blinded, placebo-controlled clinical trial with 86 healthy adult participants with untreated elevated LDL cholesterol ≥ 160 mg/dL was conducted. Participants were randomly allocated to either placebo or LP (1.2 x10 9 CFU/d) for 12 weeks. LDL, HDL, TC and triglycerides (TG), cardiovascular parameters (blood pressure, arterial stiffness) and fecal gut microbiota composition (16S rRNA gene sequencing) were assessed at baseline and after 12 weeks. Both groups were comparable regarding age, sex and LDL-C at baseline. LDL-C decreased (mean decrease − 6.6 mg/dl ± -14.0 mg/dl, P time*intervention = 0.006) in the LP group but not in the placebo group. No effects were observed on HDL, TG or cardiovascular parameters or overall gut microbiota composition. Responders to LP intervention (> 5% LDL-C reduction) were characterized by higher BMI, pronounced TC reduction, higher abundance of fecal Roseburia and lower abundance of Oscillibacter . In conclusion, 12-week of L. plantarum intake moderately reduced LDL-C and TC as compared to placebo. LDL-C lowering efficacy of L. plantarum strains may potentially be dependent on individual difference in the gut microbiota. Trial registration: DRKS00006189, dated 17/12/2019. gut microbiota low-density lipoprotein dyslipidemia probiotic Figures Figure 1 Figure 2 Introduction Cardiovascular diseases (CVD) are the leading cause of death worldwide [ 1 ]. The WHO estimated that more than 17.9 million peopled died from CVD in 2019. The most common form of CVD are coronary heart diseases caused by atherosclerosis. Epidemiological studies consistently show that increased plasma cholesterol and mainly the low-density lipoprotein cholesterol (LDL-C) fraction are associated with a high risk of developing atherosclerosis and myocardial infarction [ 2 ]. In moderate hypercholesterolemia (i.e., LDL-C level of ≥ 160 mg/dl - ≤ 200 mg/dl) and absence of CVD risk factors (e.g., smoking, hypertension, metabolic disorders), lifestyle modifications as nutritional adaptations can effectively reduce LDL-C level back to a normal range. The European society of cardiology (ESC) reported pharmacological intervention as the first choice of therapy for dyslipidemia if lifestyle modifications are not sufficient to reduce the atherosclerotic risk [ 3 ]. However, the desire for non-pharmacological intervention strategies is high, especially due to the side effects of statins affecting quality of life [ 3 ]. Among the nutritional modifications, probiotics have been implicated to beneficially modulate cholesterol metabolism. Probiotics are living microorganisms (e.g., Lactobacillus or Bifidobacterium spp.) that may colonize the gastrointestinal tract and confer beneficial health effects [ 4 ]. Consumption of probiotics mainly containing Lactobacillus plantarum and Lactobacillus reuteri species reduces circulating LDL-C concentrations in hypercholesteremic patients as shown in meta-analyses [ 5 – 7 ]. In vitro studies have suggested that the mechanism of action is based on the microbial expression of bile salt hydrolases (BSH), which are capable of deconjugating bile acids [ 8 , 9 ]. Similar to the actions of pharmacological bile acid sequestrants, microbial deconjugation of bile acids interferes with recycling of bile, which stimulates the hepatic de novo bile acid synthesis and may ultimately lead to lower circulating LDL-C concentrations [ 9 ]. Other potential probiotic LDL-lowering mechanisms include incorporation of cholesterol in the microbial cell membranes or microbial metabolism of cholesterol to coprostanol [ 10 ]. Nonetheless, the beneficial effects of probiotics appear to be strain specific [ 11 ]. Lactobacillus plantarum CECT 7527, 7528 and 7529 strains have shown promising cholesterol lowering efficacy in vitro [ 12 ] and in participants with dyslipidemia [ 13 ]. However, beneficial host effects of probiotics rely partly on a at least transient colonization, which is mediated by the residing host commensal gut microbiota amongst other factors [ 14 ]. While there is little evidence that probiotics actually induce shifts in the overall community structure, multi-omics studies with single Bifidobacteria strains or probiotic mixtures show that the residential gut microbiota exerts functional and phylogenetic selection on the incoming probiotic bacteria [ 15 , 16 ]. However, to date, no human study has yet investigated associations between features of the gut microbiota and cholesterol lowering effects after Lactobacillus plantarum CECT 7527, 7528 and 7529 intake. Hence, this randomized, placebo-controlled trial investigated the effect of 12-week probiotic supplementation with Lactobacillus plantarum (CECT 7527, 7528 and 7529 strains) on LDL-C and cholesterol metabolism in mildly hypercholesteremic participants (LDL-C ≥ 160mg/dl and ≤ 220mg/dl). Further, we investigated whether differences in LDL-C changes were associated with features of the residing host gut microbiota. Methods Study participants In total, 86 healthy women and men between the age of ≥ 30 and ≤ 75 years and a body mass index (BMI) of ≥ 18 kg/m 2 and ≤ 35 kg/m 2 were recruited between January 2020 to December 2021 from the general population in the region of Hanover in Lower Saxony, Germany. Participants were included based on their fasting LDL-cholesterol level (LDL-C ≥ 160mg / dl and ≤ 220mg / dl) measured at the initial screening visit. Moderate LDL-hypercholesteremia should be treated with lifestyle modifications in case of no concomitant risk factor for CVD. Relevant risk factors of CVD were defined as exclusion criteria: fasting triglycerides ≥ 220 mg/dl; BMI > 35 kg/m 2 , severe gastrointestinal or cardiovascular diseases, intake of immunosuppressive or chronic corticosteroids or known allergy or intolerance to ingredients contained in the preparation. Further we excluded subjects using lipid and cholesterol-lowering drugs, taking dietary supplements that affect the lipid- and cholesterol metabolism, regular intake of laxatives and intake of antibiotics three months prior to the study. This study was approved by the Ethics Committee of the Medical Association of Lower Saxony (Hanover, Germany). The study is official registered at the German Register of Clinical Studies (DRKS) with the identification number DRKS00006189 and was conducted in accordance with the guidelines of the Declaration of Helsinki (revised version, October 2008, Seoul, South Korea). Written informed consent was obtained from all participants. Study Design This study was a double blind, randomized, placebo-controlled nutritional intervention trial. Participants with fasting LDL-C levels between ≥ 160 mg / dl and ≤ 220 mg / dl were randomly allocated to 12-week intake of either Lactobacillus mixture ("Lacto" group) or placebo capsules. Lactobacillus capsules contained 100 mg bacterial mixture containing 1.2 x 10 9 CFU of L . plantarum CECT7527 (KABP011), L . plantarum CECT7528 (KABP012), L . plantarum CECT7529 (KABP013) in portion 1:1:1 each,340 mg maltodextrin; 0.5 mg silicon dioxide (release agent) and 95 mg capsule shell (hydroxypropyl cellulose, dyed with titanium dioxide). Placebo capsule contained 440 mg maltodextrin, 0.5 mg silicon dioxide and 95 mg capsule shell (hydroxypropyl cellulose, dyed with titanium dioxide). Manufacturing procedures of the Lactobacillus preparation have been described elsewhere [ 17 ]. The sample size was n = 42 per group calculated based on a previous study with hypercholesteremic patients [ 13 ], estimating a moderate effect size of 0.3, a significance level of 5% (two-sided) at a power of 80%. An additional 15% drop out rate was considered in the inclusion yielding a total n = 50 per group. The randomization was stratified by age and sex by an in independent person otherwise not involved in the study. Both placebo and Lactobacillus strains were provided in identical looking capsules and packaging. Participants were instructed to ingest one capsule per day after a meal intake for 12 weeks. Before and after the intervention period, participants were invited for an examination day. During the intervention period, participants were asked to maintain their usual diet as well as physical activity habits. Compliance was ensured by counting the number of returned capsules after the 12-week intervention period. Screening and examination days At the screening, participants were asked to come after an overnight fast (> 12 h) to the Institute of Food Science and Human Nutrition in Hanover. Eligibility criteria were assessed via a general health questionnaire and a rapid LDL-C test (Accutrend® Plus, Roche Diagnostics GmbH, Mannheim, Germany), where capillary blood drops were taken by a finger prick. Participants with fasting LDL-C concentrations between ≥ 160mg/dl and ≤ 220mg/dl were immediately included in the study and the baseline examinations were conducted. Fasting blood samples from the antecubital vein were taken for further biochemical analyses. The baseline examination included measurement of body weight, body height, waist and hip circumference, blood pressure and pulse. Body mass index was calculated by the ratio of weight to the squared height. Consequently, measurement of blood pressure and pulse were performed using volume-plethysmography (boso ABI-system 100; BOSCH & SOHN, Germany) as previously described [ 18 ]. In short, after a 5 min rest in supine position, the systolic blood pressure at the left and right posterior on both sides were measured. Biochemical blood analysis Fasting blood samples were collected in EDTA and serum monovettes (Sarstedt AG & Co., Nümbrecht, Germany). Blood samples were stored at 4°C and were transferred on the same day to an accredited and certified laboratory (Laborärztliche Arbeitsgemeinschaft für Diagnostik und Rationalisierung e.V., Hannover, Germany). Triglycerides, LDL and high-density lipoprotein cholesterol (HDL) were analyzed by a photometric method (Beckman Coulter GmbH, Krefeld, Germany). Total cholesterol and LDL/HDL-ratio were calculated from LDL and HDL values. Fecal sample collection Stool samples were collected before the baseline and the final examination day after the intervention period at home using a fecal collection kit (Süsse Labortechnik, Gudensberg, Germany) and tubes containing 3.5 ml RNASepar stabilizer solution (Biosepar GmbH, Simbach am Inn, Germany). Upon arrival at the university, fecal samples were immediately stored at − 80°C. In addition, stool consistency was documented using the Bristol stool chart [ 19 ] in addition to the time of defecation and storage conditions. Gut mircobiota sequencing 16S rRNA gene amplification of the V4 region (F515/R806) was performed according to an established protocol as previously described [ 20 ]. DNA isolation from stabilized fecal material was performed using the ZymoBIOMICS 96 MagBead DNA Kit (Freiburg, Germany) following the manufacturer’s instructions. Briefly, DNA was normalized to 25 ng/µl and used for sequencing PCR with unique 12-base Golary barcodes incorporated via specific primers (obtained from Sigma). PCR was performed using Q5 polymerase (New England Biolabs, New England Biolabs, Ipswich, Massachusetts) in triplicates for each sample, using PCR conditions of initial denaturation for 30 s at 98°C, followed by 25 cycles (10 s at 98°C, 20 s at 55°C, and 20 s at 72°C). After pooling and normalization to 10 nM, PCR amplicons were sequenced on an Illumina MiSeq platform via 250 bp paired-end sequencing (PE250). Using Usearch8.1 software package ( http://www.drive5.com/usearch/ ) the resulting reads were assembled, filtered and clustered. Sequences were filtered for low quality reads and binned based on sample-specific barcodes using QIIME v1.8.0 [ 20 ]. Merging was performed using -fastq_mergepairs – with fastq_maxdiffs 30. Quality filtering was conducted with fastq_filter (-fastq_maxee 1), using a minimum read length of 250 bp and a minimum number of reads per sample = 1000. Reads were clustered into 97% ID OTUs by open-reference OTU picking and representative sequences were determined by use of UPARSE algorithm [ 21 ]. Abundance filtering (OTUs cluster > 0.5%) and taxonomic classification were performed using the RDP Classifier executed at 80% bootstrap confidence cut off [ 22 ]. Sequences without matching reference dataset were assembled as de novo using UCLUST. Phylogenetic relationships between OTUs were determined using FastTree to the PyNAST alignment [ 23 ]. Statistical analysis Normal distribution of the data was assessed by Shapiro-Wilk test and visual inspection. Non-parametric data were log-transformed to ensure normal distribution. To detect differences between the groups at baseline, Students t-test was used for normally distributed data and the Chi-square test was applied for nominal variables. The intervention effect was determined using a repeated measures general linear model (GLM) with the within-subject factor time (t0, t12) and between-subject factor intervention (Lacto, placebo) comparison. P-values of < 0.05 were considered significant. Analysis of the clinical data was performed in SPSS (28.0.1.0 (142)). Resulting OTU absolute abundance table and mapping file were used for statistical analyses using the package phyloseq [ 24 ]. Samples were rarefied to an even sequencing depth. Alpha-diversity indices for Shannon´s index, inverse Simpson index, observed richness index and Chao1 richness index were calculated, and a paired Wilcoxon signed-rank test was used comparing time points within each group. Barplots of the relative abundance of each individual were visualized using the microviz package [ 25 ]. Samples were filtered to at least 10% of prevalence of the total samples before analysis of differential abundances. Differential abundances were compared within groups before and after the intervention with the centered log-ratio transformed abundances on phyla, family and genus level using Wilcoxon signed-rank test with FDR-adjustment for multiple testing as described in. To detect compositional differences in the microbiota between groups after the intervention, permutational multivariate analysis of variance (PERMANOVA) was conducted using generalized weighted UniFrac distance as implemented in the package vegan [ 26 ] and GUniFrac [ 27 ]. The individual participant ID was used as block factor to account for repeated measures. Multidimensional scaling ordination was used to visualize clustering of samples based on generalized UniFrac distances using the microviz package. Ellipses were drawn based on the 95% confidence limit of the standard error of points for each participant. Multivariate Association with Linear Models (MaAsLin2) [ 28 ] were used to investigate associations between in taxa abundances, responder status, LDL-C and total cholesterol changes. Differences of microbial taxa between responder and non-responder (as fixed factor) were controlled for age, sex, BMI, stool consistency and participant ID (as random effects). Associations between microbial taxa, LDL-C and total cholesterol concentrations (as fixed factor) were controlled for baseline LDL-C or total cholesterol concentrations, age, sex, BMI, stool consistency and participant ID (as random effects). Default settings of MaAsLin2 were used and q-values < 0.25 were considered significant. All microbiota statistical analysis were carried out in R (version 4.2.1). Results Baseline characteristics In total, 86 participants with mild hypercholesteremia (LDL-C ≥ 160mg / dl and ≤ 220mg / dl) were included in this study. Of these, 83 participants provided a complete stool sample from both time points before and after the intervention for 16S rRNA gene sequencing (Supplemental Fig. S1 ). Participants in the Lacto group showed a good compliance as 90% ± 4% of capsules were consumed during the study period. There were no significant differences in age, BMI, fasting glucose, blood pressure at baseline between both groups (Table 1 ). Table 1 Baseline characteristics of the study participants Variables Lacto (n = 43) Placebo (n = 43) P Sex, male/female 14/34 14/30 0.631 † Age, y 63.6 ± 7.0 63.3 ± 7.9 0.690 Weight, kg 75.1 ± 13.9 76.7 ± 13.5 0.681 Body mass index, kg/m 2 26.4 ± 4.2 26.2 ± 3.9 0.724 Fasting glucose, mmol/L 5.6 ± 0.6 5.5 ± 0.4 0.856 Waist:hip ratio 0.87 ± 0.09 0.84 ± 0.09 0.273 Systolic blood pressure, mmHg 141 ± 15 138 ± 12 0.243 Diastolic blood pressure, mmHg 88 ± 8 86 ± 7 0.429 Data are mean ± SD. Group differences were assessed using independent Student’s t-test. †Group differences in sex were assessed using Chi 2 test. LDL, low-density lipoprotein Lipid Metabolism after Lactobacillus plantarum intake We observed a significant reduction of LDL-C concentrations in the Lacto group compared to the placebo group (mean LDL-C change: Lacto group: -6.6 mg/dl ± -14.0 mg/dl; Placebo group: 2.3 mg/dL ± 13.9 md/dL; P = 0.006 for the time*intervention difference, Table 2 ), while there were no significant changes in the placebo group after the intervention. Further, total cholesterol was significantly reduced in the Lacto group (mean total cholesterol decrease − 10.4 mg/dl ± 24.2 mg/dl, P = 0.045, Table 2 ) when compared to the placebo group. We observed no differences in triglycerides, HDL-C concentrations and LDL:HDL ratio after the intervention period between both groups. Table 2 Blood lipid parameters before and after the intervention period Variables Lacto (n = 43) Placebo (n = 43) P LDL cholesterol, mg/dl Pre 190 ± 19.4 188 ± 20.6 0.006* Post 184 ± 21.2 190 ± 21.1 Total cholesterol, mg/dl Pre 280 ± 32.3 282 ± 32.4 0.045* Post 269 ± 36.5 282 ± 35.7 HDL cholesterol, mg/dl Pre 64.2 ± 15.1 64.9 ± 14.5 0.879 Post 64.1 ± 16.5 64.5 ± 14.6 Triglycerides, mg/dl Pre 114.2 ± 37.0 135.4 ± 54.5 0.775 Post 117.2 ± 39.9 136.5 ± 60.0 LDL:HDL ratio Pre 3.1 ± 0.7 3.0 ± 0.7 0.133 Post 3.0 ± 0.6 3.1 ± 0.6 Data are mean ± SD and analyzed using generalized linear models with time (pre/post) and intervention as fixed factors.* P -value represent time*intervention interaction. LDL, low-density lipoprotein, HDL, high-density lipoprotein Responder and non-responder observation in clinical data We observed great interindividual variation in LDL-C response after 12 week of Lactobacillus intake (Supplemental Fig. S 2). Therefore, we classified participants in the Lacto group into responder (i.e. > 5% LDL-C decrease after intervention, n = 20) and non-responder (i.e., ≤ 5% LDL-C decrease or no change after intervention, n = 23). The lipid profile of responders was similar to non-responders, yet responders had a higher body weight (79.9 kg ± 11.7 kg in responder vs. 71.6 kg ± 14.1 kg in non-responder, P = 0.044) and BMI (28.1 kg/m 2 ± 4.5 kg/m 2 in responder vs. 25.2 kg/m 2 ± 3.9 kg/m 2 in non-responder, P = 0.029, Supplemental Table S 1) at baseline. Responders had a significantly higher reduction in total cholesterol concentrations after the intervention when compared to non-responders (Supplemental Table S 1), while fasting triglycerides, HDL-C and LDL:HDL ratio were not significantly different between responder and non-responder in the Lacto group. Gut microbiota composition after Lactobacillus plantarum intake In total, 16S rRNA gene sequencing of fecal samples was analyzed in 43 participants of the Lacto group and 40 participants of the placebo group. A total of 2,976,226 reads with a mean of 17,715 reads of the V4 region of the 16S rRNA gene were obtained. The alpha diversity indices were not significantly different in the Lacto group as compared to the placebo group after the intervention (paired Wilcoxon P > 0.5, Supplemental Fig. S 4. There were no major taxonomic compositional changes of the gut microbiota between the groups after the intervention period as assessed using generalized weighted UniFrac distances (PERMANOVA P time*intervention > 0.5, Fig. 1 a, b). When comparing differential abundances on phylum, family or genus level before and after the intervention, no taxa was significantly different (paired Wilcoxon FDR-adjusted P > 0.05) within the groups (Fig. 1 c, d Supplemental Table S 1). Lactobacillus abundance at genus level was not significantly different at baseline or after the intervention between the groups (Supplementary Fig. S 3). Difference in gut microbiota in responder vs. non-responder Further, we investigated differences in the gut microbiota composition of responders and non-responders in the Lacto group. Alpha diversity indices were not different between responders and non-responders after Lactobacillus plantarum intake (Supplemental Fig. S 5). Further, we did not observe differences in beta diversity indices using generalized weighted UniFrac distances over time (PERMANOVA, time*responder, P > 0.5). However, the gut microbiota composition of responder was significantly different to the gut microbiota composition of non-responders independent of the intervention (PERMANOVA responder P = 0.03, Fig. 2 a). Using multivariate linear models (MaAsLin2) with the covariates age, sex, BMI, participant ID and stool consistency, responders had consistently higher relative abundance of the Roseburia (MaAsLin2 coeff 1.01, q = 0.05) and lower abundances of Oscillibacter (MaAsLin2 coeff − 1.36, q = 0.05) on genus level independent of the intervention as compared to non-responders (Fig. 2 b, Supplemental Table S 3). To confirm these associations, we further investigated whether changes of LDL-C and total cholesterol concentration as continuous variables were associated with these differential abundant taxa. Higher relative abundances of Oscillibacter were both associated with higher LDL-C and higher total cholesterol concentrations after the intervention in the Lacto group using multivariate models adjusted for baseline LDL-C or total cholesterol respectively, age, sex, BMI, participant ID and stool consistency (Fig. 2 c, d, Supplemental Table S 3). Conversely, lower concentrations of total cholesterol but not LDL-C after the Lactobacillus plantarum intake was associated with higher Roseburia abundance ( Fig. 2 e, Supplemental Table S 3). Discussion The primary aim of this randomized, placebo-controlled study was to investigate the effect of 12 weeks intake of Lactobacillus plantarum strains CECT7527, CECT7528, and CECT7529 on LDL-C concentrations in patients with mild hypercholesterolemia (≥ 160 mg/dl LDL-C). A secondary aim was the investigation of associations between the LDL-C response and the gut microbiota composition. In contrast to a previous intervention study with L. plantarum strains CECT7527, CECT7528, and CECT7529 in patients with hypercholesterolemia [ 13 ], we observed a moderate but significant decrease of -3.2 % i LDL-C after 12 week supplementation of L. plantarum strains CECT7527, CECT7528, and CECT7529. Remarkably, the reduction in total cholesterol (-3.3%) was less pronounced in the present study as compared to a reduction of -13% in total cholesterol as reported previously [ 13 ]. However, differences in cholesterol metabolism may partly account for the observed differential study outcomes, as the dyslipidemia both in LDL-C and total cholesterol was more severe in the present study population as compared to the previous study [ 13 ]. Even though the observed reduction of LDL-C and cholesterol are relatively minor, a reduction of 1% of cholesterol may already lead to a 2–3 % reduced rsk of developing coronary heart disease [ 29 ]. A more recent meta-analysis suggested that a decrease of 10 mg/dl LCL-C reduced the relative risk of coronary heart diseases by 7.1% [ 16 ]. We did not detect differences in the abundance of Lactobacillus on genus level after the intervention, which may be explained by the sampling site as the gut microbiota sampled from feces rather resemble the composition in the distal colon [ 30 ]. Of note, successful colonization of probiotics has been observed in more proximal intestinal niches as well as along spatial gradients from gut mucosa to gut lumen [ 15 , 30 ]. Besides, there is a clear microbial succession along the intestine [ 31 ]. Like the ripples of a wave fade as distance increases from the perturbation, microbiota could change in the ileum and remain totally unchanged in the distal colon. In addition, we did not observe any shifts in the overall microbial community after the intervention, thus resilience and stability of the residing gut microbiota might have hampered colonization of incoming, possibly non-native Lactobacillus strains [ 32 ]. The observed - though moderate - cholesterol lowering effect may be in place even when Lactobacillus plantarum presence is only transient. Interestingly, there were great interindividual differences in LDL-C response between the participants, hence, we further explored differences in responder (> 5% LDL-C decrease) vs. non-responder ≤ 5% LDL-C or no change) in the Lacto group. Surprisingly, responder and non-responder did not differ with regards to their baseline LDL-C concentrations, total cholesterol or triglyceride, thus, ruling out the possibility that high initial cholesterol or triglyceride concentrations account for the observed differences LDL-C regarding reduction after L. plantarum intake as suggested previously [ 7 ]. In addition, responders had a significantly higher BMI when compared to non-responders. As elevated bile acids are frequently observed in overweight/obesity [ 33 , 34 ], BSH activity of Lactobacillus plantarum strains may be more pronounced in the presence of bile acid abundancy in overweight/obese responders and hence promote cholesterol scavenging [ 9 ]. In addition, microbiota-mediated factors may be responsible for the observed interindividual differences of LDL-C lowering after probiotic intake as indicated previously [ 15 ]. The gut microbiota composition between responders vs. non-responders differed independent of the intervention period, after adjusting for BMI and other possible confounding factors. Responders had consistently higher fecal abundances of the Roseburia , a beneficial butyrate- producing gut commensal [ 35 ]. The abundant presence of Roseburia in responders vs. non-responders may constitute a favorable niche for the incoming L. plantarum , as Roseburia and Lactobacillus may interact through cross-feeding networks involving acetate and butyrate [ 36 ]. Moreover, responders had lower abundances of the gut commensal Oscillibacter , a putative butyrate and valerate producer [ 37 ], which was correlated with reductions in LDL-C and total cholesterol after the intervention. The latter supports previous findings reporting lower abundance of Oscillibacter in lean as compared to obese participants [ 38 – 40 ]. Of note, Oscillibacter presence has been negatively associated with HDL concentrations in a observational cohort of healthy and hypercholesteremic men [ 41 ]. Contrary, mendelian randomization analyses describe a causal relationship of Oscillibacter abundance in feces and reduced plasma triglycerides in a large Chinese cohort [ 42 ]. However, due to the lack of mechanistic insights on Oscillibacter , it is difficult to set these controversial associations in a physiological context. To conclude, responders characterized by markedly reduced LDL-C and total cholesterol after L. plantarum intake differed to non-responder with regards to BMI, body weight, abundance of Roseburia and Oscillibacter . This study has several limitations. First, we did not monitor dietary intake before and after the intervention, which might have changed during the intervention period and thus influence clinical outcomes. Secondly, the study was conducted during two major COVID-19 lock down periods in Germany (Jan 2020 to Dec 2021), thus, it is likely that dietary habits as well as physical activity levels changed during the study period. These factors could possibly influence the difference in efficacy between our study and a previously published one using the same strains [ 13 ]. Besides, 16S rRNA gene sequencing has limitations regarding its detection sensitivity on species or even strain level, however, more sensitive methods such as qPCR with strain specified primers were unfortunately not available in the present study. In conclusion, 12 weeks of Lactobacillus plantarum strains intake have a moderate effect on lowering LDL-C and cholesterol levels in mildly hypercholesterolemic patients. Even though transiently, the LDL-lowering efficacy of the probiotic L. plantarum strains may be mediated by individual difference in the gut microbiota as we detected difference in Oscillibacter and Roseburia abundances in responder vs. non-responder. Thus, further studies should focus on elucidating characteristics of the residing gut microbiota in the context of L. plantarum intake to improve beneficial effects on lipid metabolism. Declarations Acknowledgements Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2155 - project number 390874280. Data Access Sequence data have been deposited at ENA (European Nucleotide Archive), under the accession number PRJEB57654. Authors' Contributions Felix Kerlikowsky was involved in investigation, formal analysis, data curation, visualization, drafting of the manuscript and editing. Mattea Müller was involved in formal analysis, data curation, visualization, drafting of the manuscript and editing. Theresa Greupner was involved in the conception design and investigation. Lena Amend und Till Strowig were involved in analysis and interpretation of the data. Andreas Hahn was involved in were involved in the conception design, interpretation of the data, and drafting of the manuscript. Ethics Approval and Consent to Participate This study was approved by the Ethics Committee of the Medical Association of Lower Saxony (Hanover, Germany) and official registered at the German Register of Clinical Studies (DRKS) with the identification number DRKS00006189. All subjects gave their written informed consent. Competing Interests The authors declare that they have no competing interests. References World Health Organization (WHO) (2021) Cardiovascular diseases (CVDs) Ference BA, Ginsberg HN, Graham I, et al (2017) Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J 38(32): 2459–72 [ https://doi.org/10.1093/eurheartj/ehx144][PMID : 28444290] Mach F, Baigent C, Catapano AL, et al (2019) ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. 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J Clin Endocrinol Metab 101(5): 1935–44 [ https://doi.org/10.1210/jc.2015-2583] [PMID: 26684275] Nie K, Ma K, Luo W, et al (2021) Roseburia intestinalis: A Beneficial Gut Organism From the Discoveries in Genus and Species. Front Cell Infect Microbiol 11: 757718 [ https://doi.org/10.3389/fcimb.2021.757718] [PMID: 34881193] Belenguer A, Duncan SH, Calder AG, et al (2006) Two routes of metabolic cross-feeding between Bifidobacterium adolescentis and butyrate-producing anaerobes from the human gut. Appl Environ Microbiol 72(5): 3593–9 [ https://doi.org/10.1128/AEM.72.5.3593-3599 .2006][PMID: 16672507] Katano Y, Fujinami S, Kawakoshi A, et al (2012) Complete genome sequence of Oscillibacter valericigenes Sjm18-20(T) (= NBRC 101213(T)). Stand Genomic Sci 6(3): 406–14 [ https://doi.org/10.4056/sigs. 2826118][PMID: 23408234] Hu H-J, Park S-G, Jang HB, et al (2015) Obesity Alters the Microbial Community Profile in Korean Adolescents. PLOS ONE 10(7): e0134333 [ https://doi.org/10.1371/journal.pone .0134333][PMID: 26230509] Thingholm LB, Rühlemann MC, Koch M, et al (2019) Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition. Cell Host & Microbe 26(2): 252–264.e10 [ https://doi.org/10.1016/j.chom.2019.07 .004][PMID: 31399369] Tims S, Derom C, Jonkers DM, et al (2013) Microbiota conservation and BMI signatures in adult monozygotic twins. ISME J 7(4): 707–17 [ https://doi.org/10.1038/ismej.2012.146] [PMID: 23190729] Granado-Serrano AB, Martín-Garí M, Sánchez V, et al (2019) Faecal bacterial and short-chain fatty acids signature in hypercholesterolemia. Sci Rep; 9(1): 1772 [ https://doi.org/10.1038/s41598-019-38874 -3][PMID: 30742005] Liu X, Tong X, Zou Y, et al (2022) Mendelian randomization analyses support causal relationships between blood metabolites and the gut microbiome. Nat Genet 54(1): 52–61 [ https://doi.org/10.1038/s41588-021-00968 -y][PMID: 34980918] Additional Declarations No competing interests reported. Supplementary Files Supplementalofthemanuscript.docx Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2023 Read the published version in Probiotics and Antimicrobial Proteins → Version 1 posted Editorial decision: Major revision 26 Jul, 2023 Reviews received at journal 18 Jul, 2023 Reviewers agreed at journal 05 Jun, 2023 Reviews received at journal 25 May, 2023 Reviewers agreed at journal 08 May, 2023 Reviewers invited by journal 08 May, 2023 Submission checks completed at journal 08 May, 2023 Editor assigned by journal 08 May, 2023 First submitted to journal 04 May, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2892874","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":198223223,"identity":"9c0fb096-e19f-4c4c-91b2-49351c41d7c9","order_by":0,"name":"Felix Kerlikowsky","email":"data:image/png;base64,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","orcid":"","institution":"Leibniz University Hannover","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Felix","middleName":"","lastName":"Kerlikowsky","suffix":""},{"id":198223226,"identity":"eb59f2c2-b556-4e7f-82b4-48f3d8e3ea90","order_by":1,"name":"Mattea Müller","email":"","orcid":"","institution":"Leibniz University Hannover","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mattea","middleName":"","lastName":"Müller","suffix":""},{"id":198223229,"identity":"dd571280-5658-4715-910b-5f1a9dd143f0","order_by":2,"name":"Theresa Greupner","email":"","orcid":"","institution":"Leibniz University Hannover","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Theresa","middleName":"","lastName":"Greupner","suffix":""},{"id":198223231,"identity":"4e8a5487-731a-46f5-8e81-1bc457e466a0","order_by":3,"name":"Lena Amend","email":"","orcid":"","institution":"Helmholtz Center for Infection Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lena","middleName":"","lastName":"Amend","suffix":""},{"id":198223235,"identity":"defc2c5e-337e-4f98-b6be-e25c6a8a3b2e","order_by":4,"name":"Till Strowig","email":"","orcid":"","institution":"Helmholtz Center for Infection Research","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Till","middleName":"","lastName":"Strowig","suffix":""},{"id":198223236,"identity":"5b088a00-8426-4317-aa0a-ba2b36486dca","order_by":5,"name":"Andreas Hahn","email":"","orcid":"","institution":"Leibniz University Hannover","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Hahn","suffix":""}],"badges":[],"createdAt":"2023-05-04 08:14:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2892874/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2892874/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12602-023-10191-2","type":"published","date":"2023-11-28T15:01:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":36786452,"identity":"eea9b80f-f3fd-4f11-9a96-24a6433f3981","added_by":"auto","created_at":"2023-05-10 17:47:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1391719,"visible":true,"origin":"","legend":"\u003cp\u003eGut microbiota composition in the Lacto (n=43) and placebo group (n=43) before (pre) and after (post) the intervention. Multidimensional scaling (MDS) of generalized UniFrac distances between the gut microbiotas of the Lacto group (\u003cstrong\u003ea\u003c/strong\u003e) and placebo group (\u003cstrong\u003eb\u003c/strong\u003e) color-coded by time points (PERMANOVA (genus level) time*intervention P\u0026gt;0.05). Ellipses represent 95% confidence intervals around the centroid of each time point. Bar plots show the top 15 taxa at genus level at both time points per individual in the Lacto (\u003cstrong\u003ec\u003c/strong\u003e) and placebo group (\u003cstrong\u003ed\u003c/strong\u003e). Numbers in the labels represent participant ID\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2892874/v1/565199929002efae6e18d7dc.png"},{"id":36786451,"identity":"fa9ae00a-e414-4c93-855d-30ed3d718507","added_by":"auto","created_at":"2023-05-10 17:47:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":817476,"visible":true,"origin":"","legend":"\u003cp\u003eGut microbiota difference in responder vs non-responder and association with clinical parameters. (\u003cstrong\u003ea)\u003c/strong\u003e Multidimensional scaling (MDS) of generalized UniFrac distances between the gut microbiota of responder vs. non-responder (PERMANOVA (genus level), Responder R\u003csup\u003e2\u003c/sup\u003e= 9%, P= 0.03). Ellipses represent 95% confidence intervals. (\u003cstrong\u003eb)\u003c/strong\u003e Box plots showing the relative abundance of Roseburia and Oscillibacter in responders and non-responders before and after the intervention. Scatter plots of changes (post vs. pre) of LDL-C (\u003cstrong\u003ec\u003c/strong\u003e) and total cholesterol (\u003cstrong\u003ed,e\u003c/strong\u003e) and the relative abundance of Oscillibacter and Roseburia on both time points after adjusting for covariates (baseline LDL-C or total cholesterol respectively, age, sex, BMI, stool consistency, participant ID). FDR-adjusted P values from MaAsLin models and coefficients are shown in each panel. Resp: responder, Nonresp: Non-responder, VAR_LDL: change in LDL-C in mg/dl compared to baseline, VAR_CT: change in total cholesterol in mg/dl compared to baseline\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2892874/v1/956012e8f5ee0a519db61595.png"},{"id":47561135,"identity":"9083f24c-a0e3-4178-a6af-11eaa754e9b3","added_by":"auto","created_at":"2023-12-04 15:09:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1892058,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2892874/v1/f117bbc0-9cce-4a12-a7d6-885b1a524508.pdf"},{"id":36786740,"identity":"b447ef0b-0a5f-4519-b0d9-e1ac344ec8b2","added_by":"auto","created_at":"2023-05-10 17:55:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":704546,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalofthemanuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-2892874/v1/cf7927000646150feada8288.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distinct microbial taxa are associated with LDL-cholesterol reduction after 12 weeks of Lactobacillus plantarum intake in mild hypercholesterolemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases (CVD) are the leading cause of death worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The WHO estimated that more than 17.9\u0026nbsp;million peopled died from CVD in 2019. The most common form of CVD are coronary heart diseases caused by atherosclerosis. Epidemiological studies consistently show that increased plasma cholesterol and mainly the low-density lipoprotein cholesterol (LDL-C) fraction are associated with a high risk of developing atherosclerosis and myocardial infarction [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In moderate hypercholesterolemia (i.e., LDL-C level of \u0026ge;\u0026thinsp;160 mg/dl - \u0026le; 200 mg/dl) and absence of CVD risk factors (e.g., smoking, hypertension, metabolic disorders), lifestyle modifications as nutritional adaptations can effectively reduce LDL-C level back to a normal range. The European society of cardiology (ESC) reported pharmacological intervention as the first choice of therapy for dyslipidemia if lifestyle modifications are not sufficient to reduce the atherosclerotic risk [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the desire for non-pharmacological intervention strategies is high, especially due to the side effects of statins affecting quality of life [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Among the nutritional modifications, probiotics have been implicated to beneficially modulate cholesterol metabolism. Probiotics are living microorganisms (e.g., \u003cem\u003eLactobacillus\u003c/em\u003e or \u003cem\u003eBifidobacterium\u003c/em\u003e spp.) that may colonize the gastrointestinal tract and confer beneficial health effects [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Consumption of probiotics mainly containing \u003cem\u003eLactobacillus plantarum\u003c/em\u003e and \u003cem\u003eLactobacillus reuteri\u003c/em\u003e species reduces circulating LDL-C concentrations in hypercholesteremic patients as shown in meta-analyses [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. \u003cem\u003eIn vitro\u003c/em\u003e studies have suggested that the mechanism of action is based on the microbial expression of bile salt hydrolases (BSH), which are capable of deconjugating bile acids [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similar to the actions of pharmacological bile acid sequestrants, microbial deconjugation of bile acids interferes with recycling of bile, which stimulates the hepatic \u003cem\u003ede novo\u003c/em\u003e bile acid synthesis and may ultimately lead to lower circulating LDL-C concentrations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Other potential probiotic LDL-lowering mechanisms include incorporation of cholesterol in the microbial cell membranes or microbial metabolism of cholesterol to coprostanol [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Nonetheless, the beneficial effects of probiotics appear to be strain specific [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. \u003cem\u003eLactobacillus plantarum\u003c/em\u003e CECT 7527, 7528 and 7529 strains have shown promising cholesterol lowering efficacy \u003cem\u003ein vitro\u003c/em\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and in participants with dyslipidemia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, beneficial host effects of probiotics rely partly on a at least transient colonization, which is mediated by the residing host commensal gut microbiota amongst other factors [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While there is little evidence that probiotics actually induce shifts in the overall community structure, multi-omics studies with single \u003cem\u003eBifidobacteria\u003c/em\u003e strains or probiotic mixtures show that the residential gut microbiota exerts functional and phylogenetic selection on the incoming probiotic bacteria [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, to date, no human study has yet investigated associations between features of the gut microbiota and cholesterol lowering effects after \u003cem\u003eLactobacillus plantarum\u003c/em\u003e CECT 7527, 7528 and 7529 intake. Hence, this randomized, placebo-controlled trial investigated the effect of 12-week probiotic supplementation with \u003cem\u003eLactobacillus plantarum\u003c/em\u003e (CECT 7527, 7528 and 7529 strains) on LDL-C and cholesterol metabolism in mildly hypercholesteremic participants (LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;160mg/dl and \u0026le;\u0026thinsp;220mg/dl). Further, we investigated whether differences in LDL-C changes were associated with features of the residing host gut microbiota.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eIn total, 86 healthy women and men between the age of ≥ 30 and ≤ 75 years and a body mass index (BMI) of ≥ 18 kg/m\u003csup\u003e2\u003c/sup\u003e and ≤ 35 kg/m\u003csup\u003e2\u003c/sup\u003e were recruited between January 2020 to December 2021 from the general population in the region of Hanover in Lower Saxony, Germany. Participants were included based on their fasting LDL-cholesterol level (LDL-C ≥ 160mg / dl and ≤ 220mg / dl) measured at the initial screening visit. Moderate LDL-hypercholesteremia should be treated with lifestyle modifications in case of no concomitant risk factor for CVD. Relevant risk factors of CVD were defined as exclusion criteria: fasting triglycerides ≥ 220 mg/dl; BMI \u0026gt; 35 kg/m\u003csup\u003e2\u003c/sup\u003e, severe gastrointestinal or cardiovascular diseases, intake of immunosuppressive or chronic corticosteroids or known allergy or intolerance to ingredients contained in the preparation. Further we excluded subjects using lipid and cholesterol-lowering drugs, taking dietary supplements that affect the lipid- and cholesterol metabolism, regular intake of laxatives and intake of antibiotics three months prior to the study. This study was approved by the Ethics Committee of the Medical Association of Lower Saxony (Hanover, Germany). The study is official registered at the German Register of Clinical Studies (DRKS) with the identification number DRKS00006189 and was conducted in accordance with the guidelines of the Declaration of Helsinki (revised version, October 2008, Seoul, South Korea). Written informed consent was obtained from all participants.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis study was a double blind, randomized, placebo-controlled nutritional intervention trial. Participants with fasting LDL-C levels between ≥ 160 mg / dl and ≤ 220 mg / dl were randomly allocated to 12-week intake of either Lactobacillus mixture (\"Lacto\" group) or placebo capsules. \u003cem\u003eLactobacillus\u003c/em\u003e capsules contained 100 mg bacterial mixture containing 1.2 x 10\u003csup\u003e9\u003c/sup\u003e CFU of \u003cem\u003eL\u003c/em\u003e. \u003cem\u003eplantarum\u003c/em\u003e CECT7527 (KABP011), \u003cem\u003eL\u003c/em\u003e. \u003cem\u003eplantarum\u003c/em\u003e CECT7528 (KABP012), \u003cem\u003eL\u003c/em\u003e. \u003cem\u003eplantarum\u003c/em\u003e CECT7529 (KABP013) in portion 1:1:1 each,340 mg maltodextrin; 0.5 mg silicon dioxide (release agent) and 95 mg capsule shell (hydroxypropyl cellulose, dyed with titanium dioxide). Placebo capsule contained 440 mg maltodextrin, 0.5 mg silicon dioxide and 95 mg capsule shell (hydroxypropyl cellulose, dyed with titanium dioxide). Manufacturing procedures of the \u003cem\u003eLactobacillus\u003c/em\u003e preparation have been described elsewhere [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The sample size was n = 42 per group calculated based on a previous study with hypercholesteremic patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], estimating a moderate effect size of 0.3, a significance level of 5% (two-sided) at a power of 80%. An additional 15% drop out rate was considered in the inclusion yielding a total n = 50 per group. The randomization was stratified by age and sex by an in independent person otherwise not involved in the study. Both placebo and \u003cem\u003eLactobacillus\u003c/em\u003e strains were provided in identical looking capsules and packaging. Participants were instructed to ingest one capsule per day after a meal intake for 12 weeks. Before and after the intervention period, participants were invited for an examination day. During the intervention period, participants were asked to maintain their usual diet as well as physical activity habits. Compliance was ensured by counting the number of returned capsules after the 12-week intervention period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eScreening and examination days\u003c/h2\u003e \u003cp\u003eAt the screening, participants were asked to come after an overnight fast (\u0026gt; 12 h) to the Institute of Food Science and Human Nutrition in Hanover. Eligibility criteria were assessed via a general health questionnaire and a rapid LDL-C test (Accutrend® Plus, Roche Diagnostics GmbH, Mannheim, Germany), where capillary blood drops were taken by a finger prick. Participants with fasting LDL-C concentrations between ≥ 160mg/dl and ≤ 220mg/dl were immediately included in the study and the baseline examinations were conducted. Fasting blood samples from the antecubital vein were taken for further biochemical analyses. The baseline examination included measurement of body weight, body height, waist and hip circumference, blood pressure and pulse. Body mass index was calculated by the ratio of weight to the squared height. Consequently, measurement of blood pressure and pulse were performed using volume-plethysmography (boso ABI-system 100; BOSCH \u0026amp; SOHN, Germany) as previously described [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In short, after a 5 min rest in supine position, the systolic blood pressure at the left and right posterior on both sides were measured.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eBiochemical blood analysis\u003c/h2\u003e \u003cp\u003eFasting blood samples were collected in EDTA and serum monovettes (Sarstedt AG \u0026amp; Co., Nümbrecht, Germany). Blood samples were stored at 4°C and were transferred on the same day to an accredited and certified laboratory (Laborärztliche Arbeitsgemeinschaft für Diagnostik und Rationalisierung e.V., Hannover, Germany). Triglycerides, LDL and high-density lipoprotein cholesterol (HDL) were analyzed by a photometric method (Beckman Coulter GmbH, Krefeld, Germany). Total cholesterol and LDL/HDL-ratio were calculated from LDL and HDL values.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFecal sample collection\u003c/h2\u003e \u003cp\u003eStool samples were collected before the baseline and the final examination day after the intervention period at home using a fecal collection kit (Süsse Labortechnik, Gudensberg, Germany) and tubes containing 3.5 ml RNASepar stabilizer solution (Biosepar GmbH, Simbach am Inn, Germany). Upon arrival at the university, fecal samples were immediately stored at − 80°C. In addition, stool consistency was documented using the Bristol stool chart [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] in addition to the time of defecation and storage conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGut mircobiota sequencing\u003c/h2\u003e \u003cp\u003e16S rRNA gene amplification of the V4 region (F515/R806) was performed according to an established protocol as previously described [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. DNA isolation from stabilized fecal material was performed using the ZymoBIOMICS 96 MagBead DNA Kit (Freiburg, Germany) following the manufacturer’s instructions. Briefly, DNA was normalized to 25 ng/µl and used for sequencing PCR with unique 12-base Golary barcodes incorporated via specific primers (obtained from Sigma). PCR was performed using Q5 polymerase (New England Biolabs, New England Biolabs, Ipswich, Massachusetts) in triplicates for each sample, using PCR conditions of initial denaturation for 30 s at 98°C, followed by 25 cycles (10 s at 98°C, 20 s at 55°C, and 20 s at 72°C). After pooling and normalization to 10 nM, PCR amplicons were sequenced on an Illumina MiSeq platform via 250 bp paired-end sequencing (PE250). Using Usearch8.1 software package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.drive5.com/usearch/\u003c/span\u003e\u003cspan address=\"http://www.drive5.com/usearch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) the resulting reads were assembled, filtered and clustered. Sequences were filtered for low quality reads and binned based on sample-specific barcodes using QIIME v1.8.0 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Merging was performed using -fastq_mergepairs – with fastq_maxdiffs 30. Quality filtering was conducted with fastq_filter (-fastq_maxee 1), using a minimum read length of 250 bp and a minimum number of reads per sample = 1000. Reads were clustered into 97% ID OTUs by open-reference OTU picking and representative sequences were determined by use of UPARSE algorithm [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Abundance filtering (OTUs cluster \u0026gt; 0.5%) and taxonomic classification were performed using the RDP Classifier executed at 80% bootstrap confidence cut off [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Sequences without matching reference dataset were assembled as \u003cem\u003ede novo\u003c/em\u003e using UCLUST. Phylogenetic relationships between OTUs were determined using FastTree to the PyNAST alignment [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eNormal distribution of the data was assessed by Shapiro-Wilk test and visual inspection. Non-parametric data were log-transformed to ensure normal distribution. To detect differences between the groups at baseline, Students t-test was used for normally distributed data and the Chi-square test was applied for nominal variables. The intervention effect was determined using a repeated measures general linear model (GLM) with the within-subject factor time (t0, t12) and between-subject factor intervention (Lacto, placebo) comparison. P-values of \u0026lt; 0.05 were considered significant. Analysis of the clinical data was performed in SPSS (28.0.1.0 (142)).\u003c/p\u003e \u003cp\u003eResulting OTU absolute abundance table and mapping file were used for statistical analyses using the package \u003cem\u003ephyloseq\u003c/em\u003e [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Samples were rarefied to an even sequencing depth. Alpha-diversity indices for Shannon´s index, inverse Simpson index, observed richness index and Chao1 richness index were calculated, and a paired Wilcoxon signed-rank test was used comparing time points within each group. Barplots of the relative abundance of each individual were visualized using the \u003cem\u003emicroviz\u003c/em\u003e package [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Samples were filtered to at least 10% of prevalence of the total samples before analysis of differential abundances. Differential abundances were compared within groups before and after the intervention with the centered log-ratio transformed abundances on phyla, family and genus level using Wilcoxon signed-rank test with FDR-adjustment for multiple testing as described in. To detect compositional differences in the microbiota between groups after the intervention, permutational multivariate analysis of variance (PERMANOVA) was conducted using generalized weighted UniFrac distance as implemented in the package \u003cem\u003evegan\u003c/em\u003e [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and \u003cem\u003eGUniFrac\u003c/em\u003e [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The individual participant ID was used as block factor to account for repeated measures. Multidimensional scaling ordination was used to visualize clustering of samples based on generalized UniFrac distances using the \u003cem\u003emicroviz\u003c/em\u003e package. Ellipses were drawn based on the 95% confidence limit of the standard error of points for each participant. Multivariate Association with Linear Models (MaAsLin2) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] were used to investigate associations between in taxa abundances, responder status, LDL-C and total cholesterol changes. Differences of microbial taxa between responder and non-responder (as fixed factor) were controlled for age, sex, BMI, stool consistency and participant ID (as random effects). Associations between microbial taxa, LDL-C and total cholesterol concentrations (as fixed factor) were controlled for baseline LDL-C or total cholesterol concentrations, age, sex, BMI, stool consistency and participant ID (as random effects). Default settings of MaAsLin2 were used and q-values \u0026lt; 0.25 were considered significant. All microbiota statistical analysis were carried out in R (version 4.2.1).\u003c/p\u003e \u003cdiv id=\"Sec10\" type=\"Results\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003ch2\u003eBaseline characteristics\u003c/h2\u003e\u003cp\u003eIn total, 86 participants with mild hypercholesteremia (LDL-C ≥ 160mg / dl and ≤ 220mg / dl) were included in this study. Of these, 83 participants provided a complete stool sample from both time points before and after the intervention for 16S rRNA gene sequencing (Supplemental Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Participants in the Lacto group showed a good compliance as 90% ± 4% of capsules were consumed during the study period. There were no significant differences in age, BMI, fasting glucose, blood pressure at baseline between both groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLacto (n = 43)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlacebo (n = 43)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, \u003cem\u003emale/female\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14/34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14/30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.631 †\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, \u003cem\u003ey\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.6 ± 7.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.3 ± 7.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, \u003cem\u003ekg\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.1 ± 13.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.7 ± 13.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, \u003cem\u003ekg/m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.4 ± 4.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.2 ± 3.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose, \u003cem\u003emmol/L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6 ± 0.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5 ± 0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist:hip ratio\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 ± 0.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84 ± 0.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure, \u003cem\u003emmHg\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141 ± 15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 ± 12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure, \u003cem\u003emmHg\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 ± 8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 ± 7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eData are mean ± SD. Group differences were assessed using independent Student’s t-test. †Group differences in sex were assessed using Chi\u003csup\u003e2\u003c/sup\u003e test. LDL, low-density lipoprotein\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eLipid Metabolism after Lactobacillus plantarum intake\u003c/h2\u003e\u003cp\u003eWe observed a significant reduction of LDL-C concentrations in the Lacto group compared to the placebo group (mean LDL-C change: Lacto group: -6.6 mg/dl ± -14.0 mg/dl; Placebo group: 2.3 mg/dL ± 13.9 md/dL; P = 0.006 for the time*intervention difference, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), while there were no significant changes in the placebo group after the intervention. Further, total cholesterol was significantly reduced in the Lacto group (mean total cholesterol decrease − 10.4 mg/dl ± 24.2 mg/dl, \u003cem\u003eP\u003c/em\u003e = 0.045, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) when compared to the placebo group. We observed no differences in triglycerides, HDL-C concentrations and LDL:HDL ratio after the intervention period between both groups.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBlood lipid parameters before and after the intervention period\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLacto (n = 43)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlacebo (n = 43)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL cholesterol, \u003cem\u003emg/dl\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190 ± 19.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e188 ± 20.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184 ± 21.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e190 ± 21.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, \u003cem\u003emg/dl\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e280 ± 32.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e282 ± 32.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.045*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269 ± 36.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e282 ± 35.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL cholesterol, \u003cem\u003emg/dl\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.2 ± 15.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.9 ± 14.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.1 ± 16.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.5 ± 14.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, \u003cem\u003emg/dl\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.2 ± 37.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.4 ± 54.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117.2 ± 39.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136.5 ± 60.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL:HDL ratio\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1 ± 0.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0 ± 0.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 ± 0.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.1 ± 0.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eData are mean ± SD and analyzed using generalized linear models with time (pre/post) and intervention as fixed factors.* \u003cem\u003eP\u003c/em\u003e-value represent time*intervention interaction. LDL, low-density lipoprotein, HDL, high-density lipoprotein\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eResponder and non-responder observation in clinical data\u003c/h2\u003e\u003cp\u003eWe observed great interindividual variation in LDL-C response after 12 week of \u003cem\u003eLactobacillus\u003c/em\u003e intake (Supplemental Fig. S 2). Therefore, we classified participants in the Lacto group into responder (i.e. \u0026gt; 5% LDL-C decrease after intervention, \u003cem\u003en\u003c/em\u003e = 20) and non-responder (i.e., ≤ 5% LDL-C decrease or no change after intervention, \u003cem\u003en\u003c/em\u003e = 23). The lipid profile of responders was similar to non-responders, yet responders had a higher body weight (79.9 kg ± 11.7 kg in responder vs. 71.6 kg ± 14.1 kg in non-responder, \u003cem\u003eP\u003c/em\u003e = 0.044) and BMI (28.1 kg/m\u003csup\u003e2\u003c/sup\u003e ± 4.5 kg/m\u003csup\u003e2\u003c/sup\u003e in responder vs. 25.2 kg/m\u003csup\u003e2\u003c/sup\u003e ± 3.9 kg/m\u003csup\u003e2\u003c/sup\u003e in non-responder, \u003cem\u003eP\u003c/em\u003e = 0.029, Supplemental Table S 1) at baseline. Responders had a significantly higher reduction in total cholesterol concentrations after the intervention when compared to non-responders (Supplemental Table S 1), while fasting triglycerides, HDL-C and LDL:HDL ratio were not significantly different between responder and non-responder in the Lacto group.\u003c/p\u003e\u003ch2\u003eGut microbiota composition after Lactobacillus plantarum intake\u003c/h2\u003e\u003cp\u003eIn total, 16S rRNA gene sequencing of fecal samples was analyzed in 43 participants of the Lacto group and 40 participants of the placebo group. A total of 2,976,226 reads with a mean of 17,715 reads of the V4 region of the 16S rRNA gene were obtained. The alpha diversity indices were not significantly different in the Lacto group as compared to the placebo group after the intervention (paired Wilcoxon \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.5, Supplemental Fig. S 4. There were no major taxonomic compositional changes of the gut microbiota between the groups after the intervention period as assessed using generalized weighted UniFrac distances (PERMANOVA P time*intervention \u0026gt; 0.5, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, b). When comparing differential abundances on phylum, family or genus level before and after the intervention, no taxa was significantly different (paired Wilcoxon FDR-adjusted \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05) within the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, d Supplemental Table S 1). \u003cem\u003eLactobacillus\u003c/em\u003e abundance at genus level was not significantly different at baseline or after the intervention between the groups (Supplementary Fig. S 3).\u003c/p\u003e\u003ch2\u003eDifference in gut microbiota in responder vs. non-responder\u003c/h2\u003e\u003cp\u003eFurther, we investigated differences in the gut microbiota composition of responders and non-responders in the Lacto group. Alpha diversity indices were not different between responders and non-responders after \u003cem\u003eLactobacillus plantarum\u003c/em\u003e intake (Supplemental Fig. S 5). Further, we did not observe differences in beta diversity indices using generalized weighted UniFrac distances over time (PERMANOVA, time*responder, \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.5). However, the gut microbiota composition of responder was significantly different to the gut microbiota composition of non-responders independent of the intervention (PERMANOVA responder \u003cem\u003eP\u003c/em\u003e = 0.03, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Using multivariate linear models (MaAsLin2) with the covariates age, sex, BMI, participant ID and stool consistency, responders had consistently higher relative abundance of the \u003cem\u003eRoseburia\u003c/em\u003e (MaAsLin2 coeff 1.01, \u003cem\u003eq\u003c/em\u003e = 0.05) and lower abundances of \u003cem\u003eOscillibacter\u003c/em\u003e (MaAsLin2 coeff − 1.36, \u003cem\u003eq\u003c/em\u003e = 0.05) on genus level independent of the intervention as compared to non-responders (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Supplemental Table S 3). To confirm these associations, we further investigated whether changes of LDL-C and total cholesterol concentration as continuous variables were associated with these differential abundant taxa. Higher relative abundances of \u003cem\u003eOscillibacter\u003c/em\u003e were both associated with higher LDL-C and higher total cholesterol concentrations after the intervention in the Lacto group using multivariate models adjusted for baseline LDL-C or total cholesterol respectively, age, sex, BMI, participant ID and stool consistency (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, d, Supplemental Table S 3). Conversely, lower concentrations of total cholesterol but not LDL-C after the \u003cem\u003eLactobacillus plantarum\u003c/em\u003e intake was associated with higher \u003cem\u003eRoseburia\u003c/em\u003e abundance \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003ee, Supplemental Table S 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary aim of this randomized, placebo-controlled study was to investigate the effect of 12 weeks intake of \u003cem\u003eLactobacillus plantarum\u003c/em\u003e strains CECT7527, CECT7528, and CECT7529 on LDL-C concentrations in patients with mild hypercholesterolemia (\u0026ge;\u0026thinsp;160 mg/dl LDL-C). A secondary aim was the investigation of associations between the LDL-C response and the gut microbiota composition.\u003c/p\u003e \u003cp\u003eIn contrast to a previous intervention study with \u003cem\u003eL. plantarum\u003c/em\u003e strains CECT7527, CECT7528, and CECT7529 in patients with hypercholesterolemia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], we observed a moderate but significant decrease of -3.2 % i LDL-C after 12 week supplementation of \u003cem\u003eL. plantarum\u003c/em\u003e strains CECT7527, CECT7528, and CECT7529. Remarkably, the reduction in total cholesterol (-3.3%) was less pronounced in the present study as compared to a reduction of -13% in total cholesterol as reported previously [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, differences in cholesterol metabolism may partly account for the observed differential study outcomes, as the dyslipidemia both in LDL-C and total cholesterol was more severe in the present study population as compared to the previous study [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Even though the observed reduction of LDL-C and cholesterol are relatively minor, a reduction of 1% of cholesterol may already lead to a 2\u0026ndash;3 % reduced rsk of developing coronary heart disease [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A more recent meta-analysis suggested that a decrease of 10 mg/dl LCL-C reduced the relative risk of coronary heart diseases by 7.1% [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe did not detect differences in the abundance of \u003cem\u003eLactobacillus\u003c/em\u003e on genus level after the intervention, which may be explained by the sampling site as the gut microbiota sampled from feces rather resemble the composition in the distal colon [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Of note, successful colonization of probiotics has been observed in more proximal intestinal niches as well as along spatial gradients from gut mucosa to gut lumen [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Besides, there is a clear microbial succession along the intestine [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Like the ripples of a wave fade as distance increases from the perturbation, microbiota could change in the ileum and remain totally unchanged in the distal colon. In addition, we did not observe any shifts in the overall microbial community after the intervention, thus resilience and stability of the residing gut microbiota might have hampered colonization of incoming, possibly non-native \u003cem\u003eLactobacillus\u003c/em\u003e strains [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The observed - though moderate - cholesterol lowering effect may be in place even when \u003cem\u003eLactobacillus plantarum\u003c/em\u003e presence is only transient. Interestingly, there were great interindividual differences in LDL-C response between the participants, hence, we further explored differences in responder (\u0026gt;\u0026thinsp;5% LDL-C decrease) vs. non-responder\u0026thinsp;\u0026le;\u0026thinsp;5% LDL-C or no change) in the Lacto group. Surprisingly, responder and non-responder did not differ with regards to their baseline LDL-C concentrations, total cholesterol or triglyceride, thus, ruling out the possibility that high initial cholesterol or triglyceride concentrations account for the observed differences LDL-C regarding reduction after \u003cem\u003eL. plantarum\u003c/em\u003e intake as suggested previously [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In addition, responders had a significantly higher BMI when compared to non-responders. As elevated bile acids are frequently observed in overweight/obesity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], BSH activity of \u003cem\u003eLactobacillus plantarum\u003c/em\u003e strains may be more pronounced in the presence of bile acid abundancy in overweight/obese responders and hence promote cholesterol scavenging [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition, microbiota-mediated factors may be responsible for the observed interindividual differences of LDL-C lowering after probiotic intake as indicated previously [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The gut microbiota composition between responders vs. non-responders differed independent of the intervention period, after adjusting for BMI and other possible confounding factors. Responders had consistently higher fecal abundances of the \u003cem\u003eRoseburia\u003c/em\u003e, a beneficial butyrate- producing gut commensal [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The abundant presence of \u003cem\u003eRoseburia\u003c/em\u003e in responders vs. non-responders may constitute a favorable niche for the incoming \u003cem\u003eL. plantarum\u003c/em\u003e, as \u003cem\u003eRoseburia\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e may interact through cross-feeding networks involving acetate and butyrate [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Moreover, responders had lower abundances of the gut commensal \u003cem\u003eOscillibacter\u003c/em\u003e, a putative butyrate and valerate producer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], which was correlated with reductions in LDL-C and total cholesterol after the intervention. The latter supports previous findings reporting lower abundance of \u003cem\u003eOscillibacter\u003c/em\u003e in lean as compared to obese participants [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Of note, \u003cem\u003eOscillibacter\u003c/em\u003e presence has been negatively associated with HDL concentrations in a observational cohort of healthy and hypercholesteremic men [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Contrary, mendelian randomization analyses describe a causal relationship of \u003cem\u003eOscillibacter\u003c/em\u003e abundance in feces and reduced plasma triglycerides in a large Chinese cohort [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, due to the lack of mechanistic insights on \u003cem\u003eOscillibacter\u003c/em\u003e, it is difficult to set these controversial associations in a physiological context. To conclude, responders characterized by markedly reduced LDL-C and total cholesterol after \u003cem\u003eL. plantarum\u003c/em\u003e intake differed to non-responder with regards to BMI, body weight, abundance of \u003cem\u003eRoseburia\u003c/em\u003e and \u003cem\u003eOscillibacter\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, we did not monitor dietary intake before and after the intervention, which might have changed during the intervention period and thus influence clinical outcomes. Secondly, the study was conducted during two major COVID-19 lock down periods in Germany (Jan 2020 to Dec 2021), thus, it is likely that dietary habits as well as physical activity levels changed during the study period. These factors could possibly influence the difference in efficacy between our study and a previously published one using the same strains [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Besides, 16S rRNA gene sequencing has limitations regarding its detection sensitivity on species or even strain level, however, more sensitive methods such as qPCR with strain specified primers were unfortunately not available in the present study.\u003c/p\u003e \u003cp\u003eIn conclusion, 12 weeks of \u003cem\u003eLactobacillus plantarum\u003c/em\u003e strains intake have a moderate effect on lowering LDL-C and cholesterol levels in mildly hypercholesterolemic patients. Even though transiently, the LDL-lowering efficacy of the probiotic \u003cem\u003eL. plantarum\u003c/em\u003e strains may be mediated by individual difference in the gut microbiota as we detected difference in \u003cem\u003eOscillibacter\u003c/em\u003e and \u003cem\u003eRoseburia\u003c/em\u003e abundances in responder vs. non-responder. Thus, further studies should focus on elucidating characteristics of the residing gut microbiota in the context of \u003cem\u003eL. plantarum\u003c/em\u003e intake to improve beneficial effects on lipid metabolism.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eFunded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany\u0026apos;s Excellence Strategy - EXC 2155 - project number 390874280.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Access\u0026nbsp;\u003c/strong\u003eSequence data have been deposited at ENA (European Nucleotide Archive), under the accession number PRJEB57654.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e Felix Kerlikowsky was involved in investigation, formal analysis, data curation, visualization, drafting of the manuscript and editing. Mattea M\u0026uuml;ller was involved in formal analysis, data curation, visualization, drafting of the manuscript and editing. Theresa Greupner was involved in the conception design and investigation. Lena Amend und Till Strowig were involved in analysis and interpretation of the data. Andreas Hahn was involved in were involved in the conception design, interpretation of the data, and drafting of the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u0026nbsp;\u003c/strong\u003eThis study was approved by the Ethics Committee of the Medical Association of Lower Saxony (Hanover, Germany) and official registered at the German Register of Clinical Studies (DRKS) with the identification number DRKS00006189. 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Nat Genet 54(1): 52\u0026ndash;61 [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41588-021-00968\u003c/span\u003e\u003cspan address=\"10.1038/s41588-021-00968\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e-y][PMID: 34980918]\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"probiotics-and-antimicrobial-proteins","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"paap","sideBox":"Learn more about [Probiotics and Antimicrobial Proteins](http://link.springer.com/journal/12601)","snPcode":"12602","submissionUrl":"https://submission.nature.com/new-submission/12602/3","title":"Probiotics and Antimicrobial Proteins","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"gut microbiota, low-density lipoprotein, dyslipidemia, probiotic","lastPublishedDoi":"10.21203/rs.3.rs-2892874/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2892874/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProbiotic microbes such as \u003cem\u003eLactobacillus\u003c/em\u003e may reduce serum total cholesterol (TC) and low-density lipoprotein (LDL) cholesterol. The objective of this study was to assess the effect of \u003cem\u003eLactobacillus plantarum\u003c/em\u003e strains CECT7527, CECT7528 and CECT7529 (LP) on the serum lipids, cardiovascular parameters and fecal gut microbiota composition in patients with mild hypercholesterolemia. A randomized, double-blinded, placebo-controlled clinical trial with 86 healthy adult participants with untreated elevated LDL cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;160 mg/dL was conducted. Participants were randomly allocated to either placebo or LP (1.2 x10\u003csup\u003e9\u003c/sup\u003e CFU/d) for 12 weeks. LDL, HDL, TC and triglycerides (TG), cardiovascular parameters (blood pressure, arterial stiffness) and fecal gut microbiota composition (16S rRNA gene sequencing) were assessed at baseline and after 12 weeks. Both groups were comparable regarding age, sex and LDL-C at baseline. LDL-C decreased (mean decrease \u0026minus;\u0026thinsp;6.6 mg/dl \u0026plusmn; -14.0 mg/dl, P\u003csub\u003etime*intervention\u003c/sub\u003e = 0.006) in the LP group but not in the placebo group. No effects were observed on HDL, TG or cardiovascular parameters or overall gut microbiota composition. Responders to LP intervention (\u0026gt;\u0026thinsp;5% LDL-C reduction) were characterized by higher BMI, pronounced TC reduction, higher abundance of fecal \u003cem\u003eRoseburia\u003c/em\u003e and lower abundance of \u003cem\u003eOscillibacter\u003c/em\u003e. In conclusion, 12-week of \u003cem\u003eL. plantarum\u003c/em\u003e intake moderately reduced LDL-C and TC as compared to placebo. LDL-C lowering efficacy of \u003cem\u003eL. plantarum\u003c/em\u003e strains may potentially be dependent on individual difference in the gut microbiota. Trial registration: DRKS00006189, dated 17/12/2019.\u003c/p\u003e","manuscriptTitle":"Distinct microbial taxa are associated with LDL-cholesterol reduction after 12 weeks of Lactobacillus plantarum intake in mild hypercholesterolemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-10 17:47:06","doi":"10.21203/rs.3.rs-2892874/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-07-26T16:38:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-07-18T22:24:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"b56d684f-c3e8-40c4-acd4-0e54474ca438","date":"2023-06-06T03:34:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-05-25T17:53:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10872404-c23d-4dc2-9561-f80dd254e4a9","date":"2023-05-08T21:34:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-05-08T18:32:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-05-08T07:23:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-05-08T07:23:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Probiotics and Antimicrobial Proteins","date":"2023-05-04T08:12:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"probiotics-and-antimicrobial-proteins","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"paap","sideBox":"Learn more about [Probiotics and Antimicrobial Proteins](http://link.springer.com/journal/12601)","snPcode":"12602","submissionUrl":"https://submission.nature.com/new-submission/12602/3","title":"Probiotics and Antimicrobial Proteins","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a8963fb7-9497-4abe-957c-0bc76f00f9b5","owner":[],"postedDate":"May 10th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-12-04T15:04:31+00:00","versionOfRecord":{"articleIdentity":"rs-2892874","link":"https://doi.org/10.1007/s12602-023-10191-2","journal":{"identity":"probiotics-and-antimicrobial-proteins","isVorOnly":false,"title":"Probiotics and Antimicrobial Proteins"},"publishedOn":"2023-11-28 15:01:32","publishedOnDateReadable":"November 28th, 2023"},"versionCreatedAt":"2023-05-10 17:47:06","video":"","vorDoi":"10.1007/s12602-023-10191-2","vorDoiUrl":"https://doi.org/10.1007/s12602-023-10191-2","workflowStages":[]},"version":"v1","identity":"rs-2892874","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2892874","identity":"rs-2892874","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}