The gut microbiota plays a critical role in maintaining physiological homeostasis. This study was designed to evaluate whether gut microbial composition affects hypertension. 16S rRNA genes obtained from cecal samples of Dahl salt-sensitive (S) and Dahl salt-resistant (R) rats were sequenced. Bacteria of the phylum Bacteroidetes were higher in the S rats compared with the R rats. Furthermore, the family S24-7 of the phylum Bacteroidetes and the family Veillonellaceae of the phylum Firmicutes were higher in the S rats compared with the R rats. Analyses of the various phylogenetic groups of cecal microbiota revealed significant differences between S and R rats. Both strains were maintained on a high-salt diet, administered antibiotics for ablation of microbiota, transplanted with S or R rat cecal contents, and monitored for blood pressure (BP). Systolic BP of the R rats remained unaltered irrespective of S or R rat cecal transplantation. Surprisingly, compared with the S rats given S rat cecal content, systolic BP of the S rats given a single bolus of cecal content from R rats was consistently and significantly elevated during the rest of their life, and they had a shorter lifespan. A lower level of fecal bacteria of the family Veillonellaceae and increased plasma acetate and heptanoate were features associated with the increased BP observed in the S rats given R rat microbiota compared with the S rats given S rat microbiota. These data demonstrate a link between microbial content and BP regulation and, because the S and R rats differ in their genomic composition, provide the necessary basis to further examine the relationship between the host genome and microbiome in the context of BP regulation in the Dahl rats.

Background: Despite available clinical management strategies, chronic kidney disease (CKD) is associated with severe morbidity and mortality worldwide, which beckons new solutions. Host-microbial interactions with a depletion of Faecalibacterium prausnitzii in CKD are reported. However, the mechanisms about if and how F prausnitzii can be used as a probiotic to treat CKD remains unknown. Methods: We evaluated the microbial compositions in 2 independent CKD populations for any potential probiotic. Next, we investigated if supplementation of such probiotic in a mouse CKD model can restore gut-renal homeostasis as monitored by its effects on suppression on renal inflammation, improvement in gut permeability and renal function. Last, we investigated the molecular mechanisms underlying the probiotic-induced beneficial outcomes. Results: We observed significant depletion of Faecalibacterium in the patients with CKD in both Western (n=283) and Eastern populations (n=75). Supplementation of F prausnitzii to CKD mice reduced renal dysfunction, renal inflammation, and lowered the serum levels of various uremic toxins. These are coupled with improved gut microbial ecology and intestinal integrity. Moreover, we demonstrated that the beneficial effects in kidney induced by F prausnitzii -derived butyrate were through the GPR (G protein-coupled receptor)-43. Conclusions: Using a mouse CKD model, we uncovered a novel beneficial role of F prausnitzii in the restoration of renal function in CKD, which is, at least in part, attributed to the butyrate-mediated GPR-43 signaling in the kidney. Our study provides the necessary foundation to harness the therapeutic potential of F prausnitzii for ameliorating CKD.

Dietary salt reduction and exercise are lifestyle modifications for salt-sensitive hypertensives. While exercise has prominent metabolic effects, salt has an adverse effect on metabolic syndrome, of which hypertension is a hallmark. We hypothesized that dietary salt impacts metabolism in a salt-sensitive model of hypertension. An untargeted metabolomic approach demonstrates lower circulating levels of the ketone body, beta-hydroxybutyrate (βOHB), in high salt-fed hypertensive rats. Despite the high salt intake, specific rescue of βOHB levels by nutritional supplementation of its precursor, 1,3-butanediol, attenuates hypertension and protects kidney function. This beneficial effect of βOHB was likely independent of gut-microbiotal and Th17-mediated effects of salt and instead facilitated by βOHB inhibiting the renal Nlrp3 inflammasome. The juxtaposed effects of dietary salt and exercise on salt-sensitive hypertension, which decrease and increase βOHB respectively, indicate that nutritional supplementation of a precursor of βOHB provides a similar benefit to salt-sensitive hypertension as exercise.

Cardiovascular disease (CVD) is the number one leading cause for human mortality. Besides genetics and environmental factors, in recent years, gut microbiota has emerged as a new factor influencing CVD. Although cause-effect relationships are not clearly established, the reported associations between alterations in gut microbiota and CVD are prominent. Therefore, we hypothesized that machine learning (ML) could be used for gut microbiome–based diagnostic screening of CVD. To test our hypothesis, fecal 16S ribosomal RNA sequencing data of 478 CVD and 473 non-CVD human subjects collected through the American Gut Project were analyzed using 5 supervised ML algorithms including random forest, support vector machine, decision tree, elastic net, and neural networks. Thirty-nine differential bacterial taxa were identified between the CVD and non-CVD groups. ML modeling using these taxonomic features achieved a testing area under the receiver operating characteristic curve (0.0, perfect antidiscrimination; 0.5, random guessing; 1.0, perfect discrimination) of ≈0.58 (random forest and neural networks). Next, the ML models were trained with the top 500 high-variance features of operational taxonomic units, instead of bacterial taxa, and an improved testing area under the receiver operating characteristic curves of ≈0.65 (random forest) was achieved. Further, by limiting the selection to only the top 25 highly contributing operational taxonomic unit features, the area under the receiver operating characteristic curves was further significantly enhanced to ≈0.70. Overall, our study is the first to identify dysbiosis of gut microbiota in CVD patients as a group and apply this knowledge to develop a gut microbiome–based ML approach for diagnostic screening of CVD.

Healthy individuals exhibit blood pressure variation over a 24-hour period with higher blood pressure during wakefulness and lower blood pressure during sleep. Loss or disruption of the blood pressure circadian rhythm has been linked to adverse health outcomes, for example, cardiovascular disease, dementia, and chronic kidney disease. However, the current diagnostic and therapeutic approaches lack sufficient attention to the circadian rhythmicity of blood pressure. Sleep patterns, hormone release, eating habits, digestion, body temperature, renal and cardiovascular function, and other important host functions as well as gut microbiota exhibit circadian rhythms, and influence circadian rhythms of blood pressure. Potential benefits of nonpharmacologic interventions such as meal timing, and pharmacologic chronotherapeutic interventions, such as the bedtime administration of antihypertensive medications, have recently been suggested in some studies. However, the mechanisms underlying circadian rhythm-mediated blood pressure regulation and the efficacy of chronotherapy in hypertension remain unclear. This review summarizes the results of the National Heart, Lung, and Blood Institute workshop convened on October 27 to 29, 2021 to assess knowledge gaps and research opportunities in the study of circadian rhythm of blood pressure and chronotherapy for hypertension.

Background: Given the rapidity with which artificial intelligence is gaining momentum in clinical medicine, current physician leaders have called for more incorporation of artificial intelligence topics into undergraduate medical education. This is to prepare future physicians to better work together with artificial intelligence technology. However, the first step in curriculum development is to survey the needs of end users. There has not been a study to determine which media and which topics are most preferred by US medical students to learn about the topic of artificial intelligence in medicine. Objective: We aimed to survey US medical students on the need to incorporate artificial intelligence in undergraduate medical education and their preferred means to do so to assist with future education initiatives. Methods: A mixed methods survey comprising both specific questions and a write-in response section was sent through Qualtrics to US medical students in May 2021. Likert scale questions were used to first assess various perceptions of artificial intelligence in medicine. Specific questions were posed regarding learning format and topics in artificial intelligence. Results: We surveyed 390 US medical students with an average age of 26 (SD 3) years from 17 different medical programs (the estimated response rate was 3.5%). A majority (355/388, 91.5%) of respondents agreed that training in artificial intelligence concepts during medical school would be useful for their future. While 79.4% (308/388) were excited to use artificial intelligence technologies, 91.2% (353/387) either reported that their medical schools did not offer resources or were unsure if they did so. Short lectures (264/378, 69.8%), formal electives (180/378, 47.6%), and Q and A panels (167/378, 44.2%) were identified as preferred formats, while fundamental concepts of artificial intelligence (247/379, 65.2%), when to use artificial intelligence in medicine (227/379, 59.9%), and pros and cons of using artificial intelligence (224/379, 59.1%) were the most preferred topics for enhancing their training. Conclusions: The results of this study indicate that current US medical students recognize the importance of artificial intelligence in medicine and acknowledge that current formal education and resources to study artificial intelligence–related topics are limited in most US medical schools. Respondents also indicated that a hybrid formal/flexible format would be most appropriate for incorporating artificial intelligence as a topic in US medical schools. Based on these data, we conclude that there is a definitive knowledge gap in artificial intelligence education within current medical education in the US. Further, the results suggest there is a disparity in opinions on the specific format and topics to be introduced.