longterm microbiome changes with antibiotics

a small study was done looking at antibiotic exposure and changes in both the salivary and gut microbiomes (see microbiome antibiotics longterm changemBio 2015 in dropbox, or http://mbio.asm.org/content/6/6/e01693-15.full.pdf+html, or  doi:10.1128/mBio.01693-15​). this study involved 2 clinical sites, one in Sweden and one in the UK, randomly assigned to placebo vs different antibiotics, and looking at changes in the microbiomes both before a single antibiotic exposure and for up to one year after. details:

--30 people in the Swedish site were randomized to placebo, ciprofloxacin, or clindamycin; 44 people in the UK were randomized to placebo, amoxicillin, or minocycline (dosage not stated)
--for the Swedish site:
        --the fecal microbiome diversity was significantly reduced for up to 4 months in the clindamycin group, and up to 12 months in the ciprofloxacin group
        --the salivary microbiomes in the same groups showed only a short-term reductions in diversity and only immediately after the clindamycin or ciprofloxacin exposure.
--for the UK site:
        ​--there was a significant reduction in microbiome diversity in both the fecal and saliva samples only at the 1 week samples after the minocycline exposure, which resolved by the 1 month analysis. amoxicillin was not associated with any change in the microbiome in saliva or the gut.
--the changes in the microbiome were largely a reduction in the production of the short-chain fatty acid butyrate in the gut, especially by clindamycin and ciprofloxacin, and butyrate production has the positive effect of inhibiting inflammation, carcinogenesis and oxidative stress in the gut. (butyrate production was decreased in correspondence to the decreased gut population of many different bacterial species, such as Faecalibacterium, etc -- see the article for the details)
--prior to antibiotic exposure they detected antibiotic resistant genes in both the saliva and gut microbiomes, even in this healthy population

so, it is unclear why the 2 microbiomes behaved differently, with the salivary microbiome being minimally affected and quite resilient and the gut microbiome having prolonged changes of up to a year after a single exposure to antibiotics (ciprofloxacin being the worst of those studied). However, this type of study reinforces the need to minimize antibiotic usage (also reinforced by the presence of antibiotic resistant genes evident in the gut of these healthy volunteers even prior to the antibiotics), and preferentially to use​ antibiotics which have lesser microbiome effects (in this case, penicillins). one wonders if there is a "tipping-point" of repeated broad-spectrum antibiotic administration leading to more permanent changes in the microbiome.


see http://gmodestmedblogs.blogspot.com/search/label/microbiome  for an array of prior blogs on the microbiome;  http://gmodestmedblogs.blogspot.com/2019/01/antibiotic-overprescribing-2-more.html  about antibiotic overprescribing; and http://gmodestmedblogs.blogspot.com/2015/02/pharyngitis-and-fusobacterium.html which argues that penicillin is the drug-of-choice for pharyngitis, over azithromycin.

Comments

Post a Comment

if you would like to receive the near-daily emails regularly, please email me at gmodest@uphams.org

Popular posts from this blog

diabetes DPP-4 inhibitors and the risk of heart failure

the FDA just came out with a safety alert about 2 DPP-4 inhibitors and the increased risk of heart failure (released 04/05/2016)(see  http://www.fda.gov/Safety/MedWatch/SafetyInformation/SafetyAlertsforHumanMedicalProducts/ucm494252.htm?source=govdelivery&utm_medium=email&utm_source=govdelivery ​ ). details: --saxagliptin and alogliptin were singled out because of 2 clinical trials in diabetic patients with heart disease. in each trial more patients taking these meds were hospitalized for heart failure vs those on placebo     --in the saxagliptin trial: 3.5% were hospitalized for heart failure (vs 2.8% on placebo). risk factors included prior heart failure or kidney disease     --in the alogliptin trial: 3.9% were hospitalized for heart failure (vs 3.3% on placebo) --recommendation by the FDA:     -- we should consider stopping these drugs if the patient develops heart failure -- i believe these trials (not cited specifically by the FDA) were the ones in the blog
Read more

cystatin c: better predictor of bad outcomes than creatinine

Image
  another article found significant discordance between creatinine and cystatin C in assessing renal function,  finding that the latter correlated most strongly with both renal and non-renal adverse clinical outcomes (see   ckd cystatin c better than creat AJKD2023  in  dropbox or doi: 10.1053/ j.ajkd.2023.04.002)   Details: -- 158,601 adults who were in the Stockholm Creatinine Measurements (SCREAM) project and had testing of creatinine and cystatin C on the same day during a healthcare encounter, from 2010-2018     -- >400,000 outpatient tests were done for both creatinine and cystatin C levels -- mean age 62, 48% female -- mean eGFR by creatinine (   eGFR cr ) 80; mean eGFR by cystatin C ( eGFR cys  ) 73 -- mean stage of renal insufficiency: 39%  eGFR cr   90+; 38%  eGFR cr  60-89; 12%  eGFR cr  45-59; 7.1%  eGFR cr  30-44; 3.2%  eGFR cr  15-29, 0.7%  eGFR cr  <15 -- mean urinary albumin-to-creat ratio: 12 mg/g -- comorbidities: hypertension 59%, diabetes 8%, coronary heart di
Read more

UPDATE: ASCVD risk factor critique

I decided to do an extensive update of my prior ASCVD (atherosclerotic cardiovascular disease) risk analysis and critique of risk calculators, with much more information about the “non-traditional” risk factors. I felt that there should be more data/clarification, since I think we are really undertreating/not preventing the most common cause of death by relying on the current ASCVD risk calculators   -- Cardiovascular risk models:      -- many of us rely on the various cardiovascular risk models to determine the appropriate approach to patients, and these are often integrated into the electronic medical records      -- these risk models have several very important limitations:          -- they are typically based on community data, eg from the Framingham study etc. But, in clinical practice, we are dealing with the individual and not the community:              -- there is very clear evidence that there are many individual risk factors that are associated with increased risk of a cardi
Read more