Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study

BackgroundIron is abundant on earth but not readily available for colonizing bacteria due to its low solubility in the human body. Hosts and microbiota compete fiercely for iron. <15% Supplemented Iron is absorbed in the small bowel, and the remaining iron is a source of dysbiosis. The gut microbiome signatures to the level of predicting anemia among low-middle-income populations are unknown. The present study was conducted to identify gut microbiome signatures that have predictive potential in association with Neutrophil to lymphocytes ratio (NLR) and Mean corpuscular volume (MCV) in anemia.MethodsOne hundred and four participants between 10 and 70 years were recruited from Odisha’s Low Middle-Income (LMI) rural population. Hematological parameters such as Hemoglobin (HGB), NLR, and MCV were measured, and NLR was categorized using percentiles. The microbiome signatures were analyzed from 61 anemic and 43 non-anemic participants using 16 s rRNA sequencing, followed by the Bioinformatics analysis performed to identify the diversity, correlations, and indicator species. The Multi-Layered Perceptron Neural Network (MLPNN) model were applied to predict anemia.ResultsSignificant microbiome diversity among anemic participants was observed between the lower, middle, and upper Quartile NLR groups. For anemic participants with NLR in the lower quartile, alpha indices indicated bacterial overgrowth, and consistently, we identified R. faecis and B. uniformis were predominating. Using ROC analysis, R. faecis had better distinction (AUC = 0.803) to predict anemia with lower NLR. In contrast, E. biforme and H. parainfluenzae were indicators of the NLR in the middle and upper quartile, respectively. While in Non-anemic participants with low MCV, the bacterial alteration was inversely related to gender. Furthermore, our Multi-Layered Perceptron Neural Network (MLPNN) models also provided 89% accuracy in predicting Anemic or Non-Anemic from the top 20 OTUs, HGB level, NLR, MCV, and indicator species.ConclusionThese findings strongly associate anemic hematological parameters and microbiome. Such predictive association between the gut microbiome and NLR could be further evaluated and utilized to design precision nutrition models and to predict Iron supplementation and dietary intervention responses in both community and clinical settings

Association of Gut Microbiome and Vitamin D Deficiency in Knee Osteoarthritis Patients: A Pilot Study

Background: Few preclinical studies have shown that Knee osteoarthritis (KOA) is linked to gut microbiome dysbiosis and chronic inflammation. This pilot study was designed to look at the gut microbiome composition in KOA patients and normal individuals with or without vitamin D deficiency (VDD, serum vitamin D <30 ng/mL). Methods: This pilot study was conducted prospectively in 24 participants. The faecal samples of all the participants were taken for DNA extraction. The V3-V4 region of 16s rRNA was amplified, and the library was prepared and sequenced on the Illumina Miseq platform. Results: The mean (±SD) age was 45.5 (±10.2) years with no defined comorbidities. Of 447 total Operational Taxonomic Units (OTUs), a differential abundance of 16 nominally significant OTUs between the groups was observed. Linear discriminate analysis (LEfSe) revealed a significant difference in bacteria among the study groups. Pseudobutyrivibrio and Odoribacter were specific for VDD, while Parabacteroides, Butyricimonas and Gordonibacter were abundant in the KOA_VDD group, and Peptococcus, Intestimonas, Delftia and Oribacterium were abundant in the KOA group. About 80% of bacterial species were common among different groups and hence labelled as core bacterial species. However, the core microbiome of KOA and VDD groups were not seen in the KOA_VDD group, suggesting that these bacterial groups were affected by the interaction of the KOA and VDD factors. Conclusion: Parabacteroides, Butyricimonas, Pseudobutyrivibrio, Odoribacter and Gordonibacter are the predominant bacteria in vitamin D deficient patients with or without KOA. Together these results indicate an association between the gut microbiome, vitamin D and knee osteoarthritis

Vitamin D status in Kancheepuram District, Tamil Nadu, India

Abstract Background Vitamin D has multifarious roles in maintenance of health and prevention of disease. The present study was undertaken to assess the vitamin D status of a rural adult south Indian population and to identify its associations with socioeconomic status and cultural practices. Methods Between June 2015 and July 2016, 424 healthy adults residing in Kattankulathur block in Tamil Nadu, India, provided venous blood samples and answered questions by personal interview. 25-hydroxy vitamin D was estimated by ELISA. Results Fifty nine (13.9%) of the 424 participants had 25OHD levels below 12 ng/mL (vitamin D deficient) and 175 (41.3%) had 25OHD levels between 12 to 20 ng/mL (vitamin D insufficiency). In univariate analysis, demographic factors associated with vitamin D status included education, occupation, socioeconomic class, and birthplace; lifestyle factors included sun exposure time, skin surface exposed to sunlight, use of sunscreen, awareness of vitamin D, and consumption of fish; and hygiene related factors included source of drinking water, availability of tap water at home, and closed toilet at home. In ordinal logistic regression, the following variables were found to be independently associated with vitamin D sufficiency: Duration of daily sun exposure below 30 min (Odds ratio 0.31, 95% confidence intervals 0.14–0.71, P = 0.006), sun exposure 30–60 min (OR 0.49, 95% CI 0.30–0.80, P = 0.004), male gender (OR 2.00, 95% CI 1.30–3.09, P = 0.002), higher level of education (OR 0.80, 95% CI 0.69–0.94, P = 0.005), non-consumption of fatty fish (OR 0.48, 95% CI 0.24–0.85, P = 0.035) and presence of closed toilet system at home (OR 0.59, 95% CI 0.37–0.93). Conclusion VDD and VDI are highly prevalent in this rural Indian community. The study identifies socioeconomic and behavior patterns that negatively impact vitamin D sufficiency, thus providing a basis for targeted intervention

Altered Nasal Microbiome in Atrophic Rhinitis: A Novel Theory of Etiopathogenesis and Therapy

Background: Atrophic rhinitis (AtR) is a chronic nasal condition with polygenic and polybacterial etiology. We investigated the clinical outcomes of honey therapy and the associated nasal microbiome in AtR. Methods: For eight weeks, a nonrandomized control trial using a nasal spray of 10% manuka honey and saline on the right and left sides of the nose was conducted on 19 primary AtR patients. A nasal endoscopy was performed and a mucosal biopsy were taken before and after the intervention. Five of the nineteen patients were selected for microbiome and GPR43 expression studies. Results: We used manuka honey to describe an effective prebiotic treatment for atrophic rhinitis. There were nine males and ten females with an average (±SD) age of 33.8 (±10.7) years. Endoscopic scores and clinical symptoms improved in honey-treated nasal cavities (p < 0.003). There was a significant decrease in inflammation, restoration of mucus glands, and increased expression of GPR43 in the nasal cavities with honey therapy. The nasal microbiome composition before and after treatment was documented. Particularly, short chain fatty acid (SCFA) producers were positively enriched after honey therapy and correlated with improved clinical outcomes like nasal crusting, congestion, and discharge. Conclusion: Our approach to treating AtR patients with manuka honey illustrated effective clinical outcomes such as (1) decreased fetid smell, (2) thickening of the mucosa, (3) decreased inflammation with healed mucosal ulcers, (4) increased concentration of the mucosal glands, (5) altered nasal microbiome, and (6) increased expression of SCFA receptors. These changes are consequent to resetting the nasal microbiome due to honey therapy

VagiBIOM Lactobacillus suppository improves vaginal health index in perimenopausal women with bacterial vaginosis: a randomized control trial

Abstract Bacterial vaginosis (BV) can cause vaginal dysbiosis that may influence general vaginal health and pregnancy complications. Balancing vaginal microbiome using Lactobacillus spp. may be a new way to prevent and treat mild BV. We conducted a randomized, double-blind, placebo-controlled pilot study aimed at evaluating the effect of the product VagiBIOM, a multi-Lactobacillus vaginal suppository, on peri- and premenopausal women with BV in restoring vaginal pH and overall vaginal health by resetting the vaginal microbiome composition. Sixty-six peri- and premenopausal women with BV symptoms were randomized with a 2:1 ratio to be treated with VagiBIOM or placebo suppositories. Vaginal pH, VAS itching score, total Nugent score, and vaginal health index (VHI) were measured. Vaginal microbiome changes before and after the treatment were analyzed by 16S rRNA sequencing and bioinformatics analysis. After 4 weeks of intervention with VagiBIOM or a placebo, the mean score for vaginal pH, VAS itching, and total Nugent score was significantly decreased from the baseline. Compared to the baseline scores, the VHI scores improved significantly following 28-day intervention (p < 0.001). Our results revealed two Lactobacillus species, L. hamsteri, and L. helveticus, as indicator species occurring differentially in the VagiBIOM-treated group. Furthermore, the regression and species network analyses revealed significant bacterial associations after VagiBIOM treatment. Lactobacillus hamsteri was positively associated with the Nugent score and negatively associated with vaginal pH. L. iners and L. salivarius were positively and inversely associated with VHI. As is typical, Bacteroides fragilis was positively associated with vaginal pH and negatively associated with the Nugent score. Interestingly, the Lactobacillus spp. diversity improved after VagiBIOM treatment. The VagiBIOM suppository treatment for peri- and premenopausal women with BV significantly relieved vaginal itching by decreasing vaginal pH and Nugent scores and improving the overall VHI after 4 weeks’ intervention. This effect was primarily the result of VagiBIOM improving vaginal Lactobacillus diversity. Trial Registration ClinicalTrials.gov registration: NCT05060029, first registration 09/28/2021: Title: A Pilot Study to Evaluate the Efficacy and Safety of Lactobacillus Species Suppositories on Vaginal Health and pH

Data_Sheet_1_Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study.docx

BackgroundIron is abundant on earth but not readily available for colonizing bacteria due to its low solubility in the human body. Hosts and microbiota compete fiercely for iron. MethodsOne hundred and four participants between 10 and 70 years were recruited from Odisha’s Low Middle-Income (LMI) rural population. Hematological parameters such as Hemoglobin (HGB), NLR, and MCV were measured, and NLR was categorized using percentiles. The microbiome signatures were analyzed from 61 anemic and 43 non-anemic participants using 16 s rRNA sequencing, followed by the Bioinformatics analysis performed to identify the diversity, correlations, and indicator species. The Multi-Layered Perceptron Neural Network (MLPNN) model were applied to predict anemia.ResultsSignificant microbiome diversity among anemic participants was observed between the lower, middle, and upper Quartile NLR groups. For anemic participants with NLR in the lower quartile, alpha indices indicated bacterial overgrowth, and consistently, we identified R. faecis and B. uniformis were predominating. Using ROC analysis, R. faecis had better distinction (AUC = 0.803) to predict anemia with lower NLR. In contrast, E. biforme and H. parainfluenzae were indicators of the NLR in the middle and upper quartile, respectively. While in Non-anemic participants with low MCV, the bacterial alteration was inversely related to gender. Furthermore, our Multi-Layered Perceptron Neural Network (MLPNN) models also provided 89% accuracy in predicting Anemic or Non-Anemic from the top 20 OTUs, HGB level, NLR, MCV, and indicator species.ConclusionThese findings strongly associate anemic hematological parameters and microbiome. Such predictive association between the gut microbiome and NLR could be further evaluated and utilized to design precision nutrition models and to predict Iron supplementation and dietary intervention responses in both community and clinical settings.</p

Image_3_Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study.JPEG

BackgroundIron is abundant on earth but not readily available for colonizing bacteria due to its low solubility in the human body. Hosts and microbiota compete fiercely for iron. MethodsOne hundred and four participants between 10 and 70 years were recruited from Odisha’s Low Middle-Income (LMI) rural population. Hematological parameters such as Hemoglobin (HGB), NLR, and MCV were measured, and NLR was categorized using percentiles. The microbiome signatures were analyzed from 61 anemic and 43 non-anemic participants using 16 s rRNA sequencing, followed by the Bioinformatics analysis performed to identify the diversity, correlations, and indicator species. The Multi-Layered Perceptron Neural Network (MLPNN) model were applied to predict anemia.ResultsSignificant microbiome diversity among anemic participants was observed between the lower, middle, and upper Quartile NLR groups. For anemic participants with NLR in the lower quartile, alpha indices indicated bacterial overgrowth, and consistently, we identified R. faecis and B. uniformis were predominating. Using ROC analysis, R. faecis had better distinction (AUC = 0.803) to predict anemia with lower NLR. In contrast, E. biforme and H. parainfluenzae were indicators of the NLR in the middle and upper quartile, respectively. While in Non-anemic participants with low MCV, the bacterial alteration was inversely related to gender. Furthermore, our Multi-Layered Perceptron Neural Network (MLPNN) models also provided 89% accuracy in predicting Anemic or Non-Anemic from the top 20 OTUs, HGB level, NLR, MCV, and indicator species.ConclusionThese findings strongly associate anemic hematological parameters and microbiome. Such predictive association between the gut microbiome and NLR could be further evaluated and utilized to design precision nutrition models and to predict Iron supplementation and dietary intervention responses in both community and clinical settings.</p

Image_1_Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study.JPEG

BackgroundIron is abundant on earth but not readily available for colonizing bacteria due to its low solubility in the human body. Hosts and microbiota compete fiercely for iron. MethodsOne hundred and four participants between 10 and 70 years were recruited from Odisha’s Low Middle-Income (LMI) rural population. Hematological parameters such as Hemoglobin (HGB), NLR, and MCV were measured, and NLR was categorized using percentiles. The microbiome signatures were analyzed from 61 anemic and 43 non-anemic participants using 16 s rRNA sequencing, followed by the Bioinformatics analysis performed to identify the diversity, correlations, and indicator species. The Multi-Layered Perceptron Neural Network (MLPNN) model were applied to predict anemia.ResultsSignificant microbiome diversity among anemic participants was observed between the lower, middle, and upper Quartile NLR groups. For anemic participants with NLR in the lower quartile, alpha indices indicated bacterial overgrowth, and consistently, we identified R. faecis and B. uniformis were predominating. Using ROC analysis, R. faecis had better distinction (AUC = 0.803) to predict anemia with lower NLR. In contrast, E. biforme and H. parainfluenzae were indicators of the NLR in the middle and upper quartile, respectively. While in Non-anemic participants with low MCV, the bacterial alteration was inversely related to gender. Furthermore, our Multi-Layered Perceptron Neural Network (MLPNN) models also provided 89% accuracy in predicting Anemic or Non-Anemic from the top 20 OTUs, HGB level, NLR, MCV, and indicator species.ConclusionThese findings strongly associate anemic hematological parameters and microbiome. Such predictive association between the gut microbiome and NLR could be further evaluated and utilized to design precision nutrition models and to predict Iron supplementation and dietary intervention responses in both community and clinical settings.</p

Image_2_Predictive association of gut microbiome and NLR in anemic low middle-income population of Odisha- a cross-sectional study.JPEG

BackgroundIron is abundant on earth but not readily available for colonizing bacteria due to its low solubility in the human body. Hosts and microbiota compete fiercely for iron. MethodsOne hundred and four participants between 10 and 70 years were recruited from Odisha’s Low Middle-Income (LMI) rural population. Hematological parameters such as Hemoglobin (HGB), NLR, and MCV were measured, and NLR was categorized using percentiles. The microbiome signatures were analyzed from 61 anemic and 43 non-anemic participants using 16 s rRNA sequencing, followed by the Bioinformatics analysis performed to identify the diversity, correlations, and indicator species. The Multi-Layered Perceptron Neural Network (MLPNN) model were applied to predict anemia.ResultsSignificant microbiome diversity among anemic participants was observed between the lower, middle, and upper Quartile NLR groups. For anemic participants with NLR in the lower quartile, alpha indices indicated bacterial overgrowth, and consistently, we identified R. faecis and B. uniformis were predominating. Using ROC analysis, R. faecis had better distinction (AUC = 0.803) to predict anemia with lower NLR. In contrast, E. biforme and H. parainfluenzae were indicators of the NLR in the middle and upper quartile, respectively. While in Non-anemic participants with low MCV, the bacterial alteration was inversely related to gender. Furthermore, our Multi-Layered Perceptron Neural Network (MLPNN) models also provided 89% accuracy in predicting Anemic or Non-Anemic from the top 20 OTUs, HGB level, NLR, MCV, and indicator species.ConclusionThese findings strongly associate anemic hematological parameters and microbiome. Such predictive association between the gut microbiome and NLR could be further evaluated and utilized to design precision nutrition models and to predict Iron supplementation and dietary intervention responses in both community and clinical settings.</p