You are what you eat?

Well, maybe your microbiome is about 3% exactly what you ate, at least.

ABSTRACT

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TITLE: Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome

AUTHORS: Niccolò Carlino, Aitor Blanco-Míguez, Michal Punčochář, Paul D. Cotter, Nicola Segata1, Edoardo Pasolli and many more...

DOI: doi: 10.1016/j.cell.2024.07.039

Published online in Cell, 2024/09/03

Highlights

With curated Food Metagenomic Data (FMD), we integrated and analyzed >2,500 food metagenomes.

Over 10,000 prokaryotic and eukaryotic MAGs uncover substantial food microbial diversity.

Food microbes account for up to an average of 3% of the adult gut microbiome.

Strain-level analysis highlights potential instances of food-to-gut microbe transmission.

Summary

Complex microbiomes are part of the food we eat and influence our own microbiome, but their diversity remains largely unexplored. Here, we generated the open access curatedFoodMetagenomicData (cFMD) resource by integrating 1,950 newly sequenced and 583 public food metagenomes. We produced 10,899 metagenome-assembled genomes spanning 1,036 prokaryotic and 108 eukaryotic species-level genome bins (SGBs), including 320 previously undescribed taxa. Food SGBs displayed significant microbial diversity within and between food categories. Extension to >20,000 human metagenomes revealed that food SGBs accounted on average for 3% of the adult gut microbiome. Strain-level analysis highlighted potential instances of food-to-gut transmission and intestinal colonization (e.g., Lacticaseibacillus paracasei) as well as SGBs with divergent genomic structures in food and humans (e.g., Streptococcus gallolyticus and Limosilactobabillus mucosae). The cFMD expands our knowledge on food microbiomes, their role in shaping the human microbiome, and supports future uses of metagenomics for food quality, safety, and authentication.

Risks for impaired post-stroke cognitive function

In a printed posted to the medRxiv preprint archive this month, I found a chart review of patients with stroke to determine factors (other than the stroke itself) which could predict cognitive impairment following the stroke admission to hospital.
The paper used patients admitted to Massachusetts General Hospital as their cases for which to form a risk factor formula, and then validated the formula against cases in a sister Boston hospital, Brighamm and Women’s Hospital.
The results are quoted as a table below:

The results found were that older age was the biggest overall risk factor (weighted for example as 4 for > 65, 8 for > 85), followed by those weighted 2: insurance of Medicare (a proxy for age over 65), mobility problems, delirium, peripheral vascular disease, and depression. Other factors were rated only at a weight of 1: these included Medicaid(a proxy for low income), prior falls, Parkinson’s, kidney disease, weight loss, and nursing home placement from the acute hospital stay.
Indeed, it seems that the factors found more significant, such as age, gait status, depression, delirium, vascular disease, are actually all also known from other, prospective studies to be risk factors for dementia in the general population, even in those without any stroke.
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ABSTRACT
Predicting post-stroke cognitive impairment using electronic health record data
Jeffrey M. Ashburner, PhD, MPH1,2; Yuchiao Chang, PhD1,2; Bianca Porneala, MS1; Sanjula D. Singh, MD, PhD3; Nirupama Yechoor, MD, MSc3; Jonathan M. Rosand, MD, MSc3; Daniel E. Singer, MD1,2; Christopher D. Anderson, MD, MMSc4; Steven J. Atlas, MD, MPH
medRxiv preprint: https://doi.org/10.1101/2024.02.02.24302240
Importance: Secondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification.
Objective: To develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records.
Design: Cohort study with patients enrolled between 2003-2016 with follow-up through 2022. Setting: Primary care practices affiliated with two academic medical centers.
Participants: Individuals 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort).
Exposures: Predictors of PSCI were ascertained from the electronic health record.
Main Outcome: The outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included. Results: The analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts.
Conclusions and Relevance: Five-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts.
Key Words: post-stroke cognitive impairment, risk stratification, risk prediction

On medical AI in 2023

“To study persons is to study beings who only exist in, or are partly constituted by a certain language”. -- Charles Taylor, Sources of the Self

Large language model-based AI (LLM’s) are the epitome of what can be constituted by language alone. They can easily take isolated linguistic philosophy to its absurd extreme.

Unfortunately, this means there is a disconnect between a statement that is correct in the context of its LLM and the statements we want, which appropriately address a scientific or clinical context in the world outside of the LLM. This results in AI dialog replies that currently make medical advisory AI impossible to trust.

Current LLM base AI’s are masters of what Harry G. Frankfurt called “bullshit.” Until AI can distinguish and eliminate fictional or obsolete diagnoses and treatments within its model, separating them from those which help the patient, it cannot be trusted with any unsupervised role in patient care.

Mountain and City

Mauna Loa November-December 2022 Eruption Livestream

Life expectancy dropped much more with the Spanish flu in 1918 than COVID-19 so far.

Dammed lies and statistics

Mark Twain wrote: “There are three kinds of lies: lies, damned lies, and statistics.”

Statistics continue to mislead the unwary in the current milieu of rapid online publication of articles and preprints. A case in point is the study published last month in the fast-tracking online medical journal Cureus. This study, entitled Regular Use of Ivermectin as Prophylaxis for COVID-19 Led Up to a 92% Reduction in COVID-19 Mortality Rate in a Dose-Response Manner: Results of a Prospective Observational Study of a Strictly Controlled Population of 88,012 Subjects, purported to show that prolonged, regular use of ivermectin lessened the incidence and mortality of COVID-19 as measured by total dose taken in the period before infection occurred.

Note that though the study title states it was prospective, planning a study in advance is not sufficient for a study to be prospective. Because subjects were only placed into their treatment groups after the study outcomes were known, this was a retrospective study.

The problem lies in the study's design: total dosage of ivermectin over the entire period before COVID infection was the regression variable, versus mortality in those infected. However, because intention-to-treat analysis was thus bypassed, the study was actually measuring the mortality in the group which took ivermectin for many months and either never got COVID-19 or got COVID-19 only after that period, versus the mortality in those who either never took ivermectin or who took ivermectin for only a short time. This means that during the early months of this study, almost all of the patients who developed COVID-19 were placed in the non-ivermectin or the "irregular user" group even if they would have been placed in the ivermectin group had they NOT come down with COVID (and thus have more time to take doses of ivermectin, as many ultimately did). In other words, the fact that some who got COVID-19 before taking ivermectin might well have taken ivermectin later in any given study period had they not gotten COVID-19 first was ignored.

Was this study "strictly controlled"? Yes, in a manner that biases it beyond repair, unless they had ALSO given intent-to-treat data! Only the authors know why their design was designed the way it was.

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REFERENCE: Kerr L, Baldi F, Lobo R, et al. (August 31, 2022) Regular Use of Ivermectin as Prophylaxis for COVID-19 Led Up to a 92% Reduction in COVID-19 Mortality Rate in a Dose-Response Manner: Results of a Prospective Observational Study of a Strictly Controlled Population of 88,012 Subjects. Cureus 14(8): e28624.

DOI: 10.7759/cureus.28624

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Here is a computer simulation (written in Julia) which demonstates the study bias mentioned above. In the program, the treatment is set to have no actual effect on the infection rate. The highly significant results are purely from the bias of excluding those who become infected from later entering a treatment arm.

using HypothesisTests

@enum TreatmentClass Untreated Irregular Excluded Regular

mutable struct Subject
    cum_dose::Float64
    treatment_class::TreatmentClass
    had_covid::Bool
    last_dose_day::Int
end

function update!(subjects::Vector{Subject}, day, pcovid = 0.001, pstart = 0.0075, pdosing = 0.25, dosage = 35)
    for subj in subjects
        if subj.had_covid
            continue
        elseif rand() < pcovid
            subj.had_covid = true
        elseif (subj.cum_dose > 0 && rand() <= pdosing  && (day > subj.last_dose_day + 14 || day == subj.last_dose_day + 1)) ||
           (subj.cum_dose == 0 && rand() < pstart)
            subj.cum_dose += dosage
            subj.last_dose_day = day
            subj.treatment_class =
               subj.cum_dose == 0 ? Untreated : subj.cum_dose >= 180 ? Regular : subj.cum_dose <= 60 ? Irregular : Excluded
        end
    end
end

function run_study(N = 10_000, duration = 150)
    population = [Subject(0.0, Untreated, false, 0) for _ in 1:N]
    unt, unt_covid, irr, irr_covid, reg, reg_covid, excluded = 0, 0, 0, 0, 0, 0, 0
    println("Population size $N, daily infection risk 0.1%")
    for day in 1:duration
        update!(population, day)
        if day % 30 == 0
            println("\nDay $day:")
            unt = count(s -> s.treatment_class == Untreated, population)
            unt_covid = count(s -> (s.treatment_class == Untreated) && s.had_covid, population)
            println("Untreated: N = $unt, with infection = $unt_covid")
            irr = count(s -> s.treatment_class == Irregular, population)
            irr_covid = count(s -> (s.treatment_class == Irregular) && s.had_covid, population)
            println("Irregular Use: N = $irr, with infection = $irr_covid")
            reg = count(s -> s.treatment_class == Regular, population)
            reg_covid = count(s -> (s.treatment_class == Regular) && s.had_covid, population)
            println("Regular Use: N = $reg, with infection = $reg_covid")
            exc = count(s -> (s.treatment_class == Excluded) && s.had_covid, population)
            println("Excluded: N = $exc")
        end
        if day == 75
            println("\nAt midpoint, Infection case percentages are:")
            println("  Untreated : ", Float16(100 * unt_covid / unt))
            println("  Irregulars: ", Float16(100 * irr_covid / irr))
            println("  Regulars  : ", Float16(100 * reg_covid / reg))
        end
    end
    println("\nAt study end, Infection case percentages are:")
    println("  Untreated : ", Float16(100 * unt_covid / unt), " of group size of $unt")
    println("  Irregulars: ", Float16(100 * irr_covid / irr), " of group size of $irr")
    println("  Regulars  : ", Float16(100 * reg_covid / reg), " of group size of $reg")
    untreated = [s.had_covid for s in population if s.treatment_class == Untreated]
    irregular = [s.had_covid for s in population if s.treatment_class == Irregular]
    regular = [s.had_covid for s in population if s.treatment_class == Regular]
    excluded = [s.had_covid for s in population if s.treatment_class == Excluded]
    println("\n\n   Final statistics:\n")
    @show KruskalWallisTest(untreated, irregular, regular, excluded)
end

run_study()