Participants were excluded from the study if they met any of the following criteria: significant alcohol intake for at least 3 consecutive months over the previous 12 months or if the quantity of alcohol consumed could not be reliably ascertained; clinical or biochemical evidence of liver diseases other than NAFLD ; metabolic and/or genetic liver disease ; clinical or laboratory evidence of systemic infection or any other clinical evidence of liver disease associated with hepatic steatosis; use of drugs known to cause hepatic steatosis for at least 3 months in the last past 6 months; history of bariatric surgery; presence of systemic infectious illnesses; females who were pregnant or nursing at the time of the study; contraindications to MRI ; any other condition which, based on the principal investigator’s opinion, may significantly affect the participant’s compliance, competence, or ability to complete the study.All participants underwent a standardized clinical research visit at the UCSD NAFLD Research Center. A detailed history was obtained from all participants. A physical exam, which included vital signs, height, weight,raspberry container and anthropometric measurements, was performed by a trained clinical investigator. Alcohol consumption was documented outside clinical visits and confirmed in the research clinic using the Alcohol Use Disorders Identifications Test and the Skinner questionnaire.
A detailed history of medications was obtained and no patient took medications known or suspected to cause steatosis or steatohepatitis. Other causes of liver disease and secondary causes of hepatic steatosis were systemically ruled out using detailed history and laboratory data. After completion of the earlier described elements of the history and physical examination, participants had a comprehensive fasting laboratory including metabolic and liver assessment previously described in references .VCTE was performed by a trained technician, using the FibroScan® 502 Touch model . VCTE measurement was obtained in the supine position with the right arm fully adducted by scanning the area of abdomen at the location of the right liver lobe during a 10 seconds breath hold. Participants were asked to fast at least 3 hours prior to the exam. The details of VCTE assessment have been previously described in references . The threshold used for the classification of cirrhosis was VCTE > 11.8 kPa as previously determined in reference . Among the first-degree relatives of proband with NAFLD-cirrhosis, 11 did not have an MRE assessment due to contraindication and the presence of advanced fibrosis was determined using a VCTE threshold> 11.8 kPa as previously determined in reference . Liver biopsy was not used for hepatic fat content and fibrosis assessment of controls and first-degree relatives as they were asymptomatic with no suspected liver disease and therefore performing a liver biopsy would have been unethical. A non-invasive, accurate quantitative imaging method was used to estimate liver fat and fibrosis.
We have previously shown that MRIPDFF is a highly precise, accurate, and reproducible non-invasive biomarker for the quantification of liver fat content . In addition, MRE is the most accurate, currently available, noninvasive quantitative biomarker of liver fibrosis . MRE has been shown to be have excellent diagnostic accuracy in differentiating between normal liver and mild fibrosis and between non-advanced fibrosis and advanced fibrosis .Participants were considered to have NAFLD-related cirrhosis if they had NAFLD according to the definition above, and have biopsy proven cirrhosis . We have previously validated that a liver stiffness cut point of >3.63 kPa on MRE provides an accuracy of 0.92 for the detection of advanced fibrosis, and it is the most accurate non-invasive test for the diagnosis of advanced fibrosis . Advanced fibrosis among first-degree relatives was determined by either imaging evidence of nodularity and presence of intraabdominal varices or other evidence imaging evidence of portal hypertension or liver stiffness assessment with MRE threshold ≥ 3.63 kPa or if MRE were not performed using transient elastography assessment with VCTE threshold ≥ 11.8 kPa. DNA extraction and 16S rRNA amplicon sequencing were done using Earth Microbiome Project standard protocols and previously described in references . In brief, DNA was extracted using the Qiagen MagAttract PowerSoil DNA kit as previously described. Amplicon PCR was performed on the V4 region of the 16S rRNA gene using the primer pair 515f to 806r with Golay error-correcting barcodes on the reverse primer. Amplicons were barcoded and pooled in equal concentrations for sequencing.
The amplicon pool was purified with the MO BIO UltraClean PCR cleanup kit and sequenced on the Illumina MiSeq sequencing platform. Sequence data were demultiplexed and minimally quality filtered using the QIIME 1.9.1 script split_libraries_fastq.py, with a Phred quality threshold of 3 and default parameters to generate per-study FASTA sequence files .To build a model capable of distinguishing samples belonging to NAFLDcirrhosis from those of non-NAFLD-controls, we developed a custom machine learning process that employed Random Forest analysis . The set of input features for model building consisted of 16S sequences and patient metadata features. Features from stool microbiome data consisted of the number of 16S sequences and the patient metadata consisted of age, gender and BMI. The first step in building an RF model consisted of training RF and then selecting features with the most important score > 0.005 in a second step. The final random forest model included the 27 bacterial features and important patient metadata for a total of 30 predictive features. Patients’ demographic, anthropometric, clinical, and biochemical characteristics were summarized. Categorical variables were shown as counts and percentages, and associations were tested using a chi-squared test or Fisher’s exact test. Normally distributed continuous variables were shown as mean , and differences between groups were analyzed using a two-independent sample t- test or Wilcoxon-Mann-Whitney test. Statistical analysis of cohort characteristics were performed using SPSS 25.0 . A two-sided p-value <0.05 was considered statistically significant.Cyrielle Caussy: study concept and design, analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript, approved final submission. Anupriya Tripathi: data analysis and figure generation, interpretation of data, drafting of the manuscript, critical revision of the manuscript, approved final submission. Gregory Humphrey: microbiome sequencing data generation, approved final submission Shirin Bassirian: patient visits, data collection, critical revision of the manuscript, approved final submission. Seema Singh: patient visits, data collection, critical revision of the manuscript, approved final submission. Claire Faulkner: patient visits, data collection, critical revision of the manuscript, approved final submission. Emily Rizo: patient visits, data collection, critical revision of the manuscript, approved final submission. Lisa Richards: patient visits, critical revision of the manuscript, approved final submission. Michael R. Downes: analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript, approved final submission. Ronald M. Evans: analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript, approved final submission. David A. Brenner: study concept and design, analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript, obtained funding, study supervision, approved final submission. Claude B. Sirlin: study concept and design, analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript, obtained funding, study supervision, approved final submission. Zhenjiang Zech Xu: data interpretation,growing raspberries in container critical revision of the manuscript, approved final submission. Rob Knight: directed microbiome sequencing and data analysis, critical revision of the manuscript, approved final submission.
Rohit Loomba: study concept and design, analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript, obtained funding, study supervision, approved final submission. The co-authors listed above supervised or provided support for the research and have given permission for the inclusion of the work in this dissertation.Obstructive sleep apnea is a common disorder characterized by episodic obstruction to breathing due to upper airway collapse during sleep. OSA has been associated with adverse cardiovascular and metabolic outcomes, although data regarding potential causal pathways are still evolving. As O2 and CO2 affect the ecology of the gut microbiota and the microbiota has been shown to contribute to various cardio-metabolic disorders, we hypothesized that OSA alters the gut ecosystem which exacerbates the downstream physiological consequences. Here, we model human OSA and its cardiovascular consequences using Ldlr-/- mice fed a high-fat diet and exposed to intermittent hypoxia and hypercapnia . The gut microbiome and metabolome were characterized longitudinally and seen to co-vary during IHH. Joint analysis of microbiome and metabolome data revealed marked compositional changes in both microbial and molecular species in the gut. Moreover, molecules altered in abundance included microbe-dependent bile acids, enterolignans and fatty acids, highlighting the impact of IHH on host-commensal co-metabolism in the gut. Thus, we present the first evidence that IHH perturbs the gut microbiome functionally, setting the stage for understanding its involvement in associated cardio-metabolic disorders.Intestinal dysbiosis marks various cardiovascular diseases comorbid with OSA. It has not been systematically studied if dysbiosis due to hypoxic stress in OSA is causally linked to these comorbidities. We take advantage of a longitudinal study design and paired ‘-omics to investigate correlations in microbial and molecular dynamics in the gut to ascertain the contribution of microbes on intestinal metabolism. We observe microbe-dependent changes in the gut metabolome that will guide future research on unrecognized mechanistic links between gut microbes and comorbidities of OSA. Additionally, we highlight novel, non-invasive biomarkers for OSA-linked pathologies. Obstructive sleep apnea afflicts nearly 12% of the adult population in the USA with a cost burden of nearly $149.6 billion, according to a recent study commissioned by the American Academy of Sleep Medicine . Timely diagnosis and treatment of OSA improves not only sleep and cognitive function but also management of comorbid cardiometabolic diseases . Therefore, identifying downstream consequences of OSA would aid in development of effective treatment modalities, reducing overall health care utilization. OSA is marked by changes in oxygen and carbon dioxide-inspired concentrations which impacts the gut microbial community . Since the gut microbiota play a key role in metabolism of dietary precursors including lipids, cholesterol and choline, it impacts the cardiometabolic health of the host . Gut dysbiosis has already been linked to an array of metabolic disorders such as hypertension, T2 diabetes, hepatic steatosis and atherosclerosis . Additionally, previous work has identified specific gut bacteria to be significantly correlated with plasma cholesterol and apolipoprotein levels . Thus, probing this commensal ecosystem may provide a valuable avenue of investigation to understand the mechanism of pathogenesis of cardiovascular consequences of OSA. In this study, we investigated the taxonomic and molecular alterations in gut microbiome that potentially mediate the interplay between OSA and related CMDs.We used atherosclerosis-prone adult mice fed high-fat diet enriched in cholesterol and milk fat to evaluate atherosclerosis risk in OSA. We previously demonstrated that IHH increases atherosclerosis plaque formation in this model . As episodic hypoxia and hypercapnia mimic the changes in blood gases that occur in OSA-driven downstream consequences , these mice were exposed to IHH or air and examined longitudinally for 6 weeks . Fecal samples, representative of the gut ecosystem, were collected at baseline and twice each week thereafter, and the microbiome and metabolome were profiled using 16S rRNA amplicon sequencing and LC-MS/MS-based untargeted mass-spectrometry, respectively. These data were processed to obtain relative abundances of microbial and molecular species per sample , which were used for comparing OSA-mimicking and control mice. First, we performed principal coordinate analysis on the microbiome and metabolome feature tables to identify major factors driving the clustering of samples. Figure 3.1 shows the PCoA plotted against time to visualize the dynamics of clustering based on gut microbiome ; Figure 3.1a and metabolome; Figure 3.1b,c as duration of IHH-exposure increases. Here, the first fecal sample represents the baseline gut composition before animals were switched to a HFD. There is a rapid shift in both microbial and molecular composition due to HFD alone, consistent with similar previous findings. Moreover, starting from a highly congruent gut composition, IHH-exposed mice significantly diverge from controls with increasing exposure duration . This demonstrates that prolonged IHH-exposure cumulatively perturbs the gut microbiome and metabolome. We tested the relationship between the two omics datasets by superimposing the principal coordinates computed from microbiome and metabolome data ; Figure 3.1d. The ordination spaces are correlated , and changes in metabolome and microbiome of samples within the treatment groups over time are proportional, suggesting microbe-dependent changes in intestinal metabolism on chronic OSA.We then tested for specific microbes and metabolites that changed with OSA.