Role of C-reactive protein in predicting the outcome of acute pancreatitis

Background: Acute pancreatitis is one of the most commonly encountered clinical entities in surgical practice and controversy still exists regarding the clinical features of acute pancreatitis. An early diagnosis, however, is regarded as mandatory for successful treatment. Over the years many Authors have proposed different scoring systems for the early assessment of the clinical evolution of acute pancreatitis. The most widely used scoring systems are often cumbersome and difficult to use in clinical practice because of their multi factorial nature. Thus, a number of unifactorial prognostic indices have been employed in routine hospital practice, such as C-reactive protein (CRP), serum amylase and serum lipase. These serum enzymes are easy to obtain in normal clinical practice and many authors consider them as reliable as multi factorial scoring systems.Methods: A hospital based observational prospective study was done with 30 patients to measure C reactive protein levels in patients of acute pancreatitis and evaluate if CRP levels predict the severity of pancreatitis.Results: In cases where CRP was raised >100 mg/dl on day 7 and beyond showed either a complication or increased duration of stay and delayed recovery. This correspondence of CRP with the clinical outcome co related well with other parameters like blood counts, serum lipase and amylase levels too.Conclusions: Hence, CRP can be a very useful uni factorial tool in assessing and thereby predicting the outcome in a case of pancreatitis.

Gut Metabolites Predict Clostridioides Difficile Recurrence

Background Clostridioides difficile infection (CDI) is the most common hospital acquired infection in the U.S., with recurrence rates >15%. Although primary CDI has been extensively linked to gut microbial dysbiosis, less is known about the factors that promote or mitigate recurrence. Moreover, previous studies have not shown that microbial abundances in the gut measured by 16S rRNA amplicon sequencing alone can accurately predict CDI recurrence. Results We conducted a prospective, longitudinal study of 53 non-immunocompromised participants with primary CDI. Stool sample collection began pre-CDI antibiotic treatment at the time of diagnosis, and continued up to eight weeks post-antibiotic treatment, with weekly or twice weekly collections. Samples were analyzed using: (1) 16S rRNA amplicon sequencing, (2) liquid chromatography/mass-spectrometry metabolomics measuring 1387 annotated metabolites, and (3) short-chain fatty acid profiling. The amplicon sequencing data showed significantly delayed recovery of microbial diversity in recurrent participants, and depletion of key anaerobic taxa at multiple time-points, including Clostridium cluster XIVa and IV taxa. The metabolomic data also showed delayed recovery in recurrent participants, and moreover mapped to pathways suggesting distinct functional abnormalities in the microbiome or host, such as decreased microbial deconjugation activity, lowered levels of endocannabinoids, and elevated markers of host cell damage. Further, using predictive statistical/machine learning models, we demonstrated that the metabolomic data, but not the other data sources, can accurately predict future recurrence at one week (AUC 0.77 [0.71, 0.86; 95% interval]) and two weeks (AUC 0.77 [0.69, 0.85; 95% interval]) post-treatment for primary CDI. Conclusions The prospective, longitudinal and multi-omic nature of our CDI recurrence study allowed us to uncover previously unrecognized dynamics in the microbiome and host presaging recurrence, and, in particular, to elucidate changes in the understudied gut metabolome. Moreover, we demonstrated that a small set of metabolites can accurately predict future recurrence. Our findings have implications for development of diagnostic tests and treatments that could ultimately short-circuit the cycle of CDI recurrence, by providing candidate metabolic biomarkers for diagnostics development, as well as offering insights into the complex microbial and metabolic alterations that are protective or permissive for recurrence.

Can We Simplify Aging?

Aging is indeed a complex process, but can it be simplified, so we could efficiently prioritize candidate anti-aging interventions and select those with largest impacts on key negative consequence of the aging, i.e., on increases in mortality risk and comorbidities with age? Here we argue that human aging and its negative consequences for health and lifespan are essentially driven by the interplay among three processes: (i) depletion of limited body reserves (e.g., of stem, immune, neural, muscle cells); (ii) inherent deficiency of cell/tissue repair mechanisms, which leads to accumulation of damage, allostatic load, and systems dysregulation; and (iii) general slowdown of physiological processes in the body (such as metabolism, proliferation and information processing) with age that results in slower responses to stressors and delayed recovery after damage (i.e., decline in resilience), which in turn contributes to increase in vulnerability to death with age. We show that the interplay among these processes can have ambivalent effects on health and longevity that should be taken into account to develop optimal anti-aging and pro-longevity strategies. In order to be efficient on the long-term, the anti-aging interventions may need to target the different causes of aging (reserve depletion, damage accumulation, and slowdown) simultaneously, to avoid undesirable trade-offs.

Gut metabolites predict Clostridioides difficile recurrence

Background Clostridioides difficile infection (CDI) is the most common hospital acquired infection in the U.S., with recurrence rates >15%. Although primary CDI has been extensively linked to gut microbial dysbiosis, less is known about the factors that promote or mitigate recurrence. Moreover, previous studies have not shown that microbial abundances in the gut measured by 16S rRNA amplicon sequencing alone can accurately predict CDI recurrence. Results We conducted a prospective, longitudinal study of 53 non-immunocompromised participants with primary CDI. Stool sample collection began pre-CDI antibiotic treatment at the time of diagnosis, and continued up to eight weeks post-antibiotic treatment, with weekly or twice weekly collections. Samples were analyzed using: (1) 16S rRNA amplicon sequencing, (2) liquid chromatography/mass-spectrometry metabolomics measuring 1387 annotated metabolites, and (3) short-chain fatty acid profiling. The amplicon sequencing data showed significantly delayed recovery of microbial diversity in recurrent participants, and depletion of key anaerobic taxa at multiple time-points, including Clostridium cluster XIVa and IV taxa. The metabolomic data also showed delayed recovery in recurrent participants, and moreover mapped to pathways suggesting distinct functional abnormalities in the microbiome or host, such as decreased microbial deconjugation activity, lowered levels of endocannabinoids, and elevated markers of host cell damage. Further, using predictive statistical/machine learning models, we demonstrated that the metabolomic data, but not the other data sources, can accurately predict future recurrence at one week (AUC 0.77 [0.71, 0.86; 95% interval]) and two weeks (AUC 0.77 [0.69, 0.85; 95% interval]) post-treatment for primary CDI. Conclusions The prospective, longitudinal and multi-omic nature of our CDI recurrence study allowed us to uncover previously unrecognized dynamics in the microbiome and host presaging recurrence, and, in particular, to elucidate changes in the understudied gut metabolome. Moreover, we demonstrated that a small set of metabolites can accurately predict future recurrence. Our findings have implications for development of diagnostic tests and treatments that could ultimately short-circuit the cycle of CDI recurrence, by providing candidate metabolic biomarkers for diagnostics development, as well as offering insights into the complex microbial and metabolic alterations that are protective or permissive for recurrence.