Posts | F. Leblanc

Rethinking saturated fats

Sat, 15 Aug 2020 00:00:00 +0000

A paper that will most likely raise a lot of controversies was published in JACC beginning of august 2020, one of the top scientific journals in cardiology. The article goes through a lot more then I cover here, but I wanted to make accessible one of the main reasons why saturated fats have been erroneously demonized (if LDL and cholesterol are all clear for you here’s my main point: Under the presumption that either LDLc or all LDLp were important contributors to atherosclerosis, a diet that showed an increase in either of these was (and still is by most) considered dangerous and to be avoided).

So what’s LDL?

LDL refers to low-density lipoprotein. This term is often confounded with cholesterol as a molecule. Let’s first describe how these 2 terms differ.

cholesterol molecule

Cholesterol is an essential molecule for your cells. Every cell’s membranes are composed of about 30% cholesterol(1). Without this, cell’s membranes would be more static, which would impair a multitude of cellular functions mediated through membrane plasticity. Cholesterol has oil-like properties, meaning it doesn’t mix well with water, which is the main ingredient in your blood. To circulate this essential molecule in the bloodstream, it needs to be packaged, enters ApoB100. This is a protein made by the liver to package cholesterol and other lipids. We will then designate these ApoB100 with their lipid cargo as lipoproteins, and stratify them on the proportion of lipid/ApoB100 in multiple classes which the densest category (least amount of lipids/ApoB100) is the LDL particle (LDLp). This is different then LDL cholesterol (LDLc) which refers to the total amount of cholesterol molecules that were present in your LDL particles, and the difference matters.

T Dayspring, T Dall, M Abuhajir - Research Reports Clin Cardiol, 2010

It turns out that within what was classified long ago as LDLp, there is a range of particle sizes. Their sizes have an impact on how likely you will be to build plaque in your vessels, the smaller the particle, the more plaque (atherosclerosis) you might build. So, while the LDLc may be associated with plaque build-up at population levels, it is a crude metric that hides a stronger signal.

Historically, we grew our understanding of the lipoprotein’s contribution to the progression of atherosclerosis from LDLc to LDLp and now small LDLp. This review; Saturated Fats and Health: A Reassessment and Proposal for Food-Based Recommendations(2), lead by one of the world’s lipoprotein experts Dr. Ronald Krauss, challenges the current nutritional guidelines on the saturated fat intake that were founded on our previous understanding of LDL’s role in atherosclerosis. Under the presumption that either LDLc or all LDLp were important contributors to atherosclerosis, a diet that showed an increase in either of these was (and still is by most) considered dangerous and to be avoided. Their extensive review makes the point that despite an increase in LDLc or overall LDLp in people that have a diet high in saturated fat from dairy, dark chocolate, or unprocessed red meat, in most cases, their all-cause mortality risk is reduced. This is not the case of people who have an increase in small LDLp, which is strongly associated with atherosclerosis progression.

Cynicism and confusion towards dietary guidelines

With changing nutritional guidelines, understandably, many become confused or cynic about them. While there are clearly some examples of policies that were influenced by entities incentivized by pure economic gain (like the cigarette or sugar industries) resulting in detrimental populational health, science has guided nutritional guidelines for the better. We enjoy longer and fuller lives because of it. The figure below shows the staggering progress science has provided to our longevity. We make decisions with the best information we have at the time, and that information keeps getter more accurate, from better technology and more research, but also changes focus on the new problems that emerge after we solved the previous ones. While the challenge at the beginning of the century was to feed enough calories to people, today this problem affects a minority of the world population. Industrial monoculture of grains was a great solution for this early 20th-century problem, but today we face a different one. Reducing the damage of 80 decades of life requires a different approach, one oriented towards increasing the nutrients in our diet (fibers, vitamins, and minerals), and reducing our simple sugar consumption.

From ourworldindata.org/life-expectancy

How should we apply this to our lives?

In the end, the take-home message is that saturated fats, as a group of molecules, don’t seem to have a causal role in our risks of cardiovascular diseases, and what matters is where these saturated fats came from (what food it was packaged into). The consumption of unprocessed foods high in saturated fats is a healthy option for most (a subset of the population may not metabolize these fats as efficiently and benefit from diets lower in saturated fats).

If you want to dive deep in this subject, here is an interview with Dr. Krauss from my favorite podcast The Drive.

BIBLIOGRAPHY

Zhang, J.; Li, Q.; Wu, Y.; Wang, D.; Xu, L.; Zhang, Y.; Wang, S.; Wang, T.; Liu, F.; Zaky, M. Y., Cholesterol content in cell membrane maintains surface levels of ErbB2 and confers a therapeutic vulnerability in ErbB2-positive breast cancer. Cell Communication and Signaling, 2019, 17 (1), 1-12.
Astrup, A.; Magkos, F.; Bier, D. M.; Brenna, J. T.; de Oliveira Otto, M. C.; Hill, J. O.; King, J. C.; Mente, A.; Ordovas, J. M.; Volek, J. S., Saturated Fats and Health: A Reassessment and Proposal for Food-based Recommendations: JACC State-of-the-Art Review. Journal of the American College of Cardiology, 2020, 76 (7), 844-857.

Literature analysis

Sat, 01 Aug 2020 00:00:00 +0000

Here I discuss the various literature packages in R and show a use case on a topic I study. Available packages;

bibliometrix: has a variety of functions returning most useful metrics you could want
scholar: useful to evaluate journals and scholars (here I use it to retrieve impact factors of journals)
pubmed.mineR: allows to query PubMed in multiple ways (great option if your query results is very high, but it’s slow and you won’t get citation counts and pubtator data)
wosr: If you have an API username and password, this might be the best option to query a large amount of papers
other packages: pubmedR, RISmed and RISmed

After spending some time exploring the different options, I found that MeSH terms were often missing, and Keywords or Keywords plus were not very informative. I ended up finding the results from pubtator. This is a NCBI Deep learning algorithm that identified and categorised 5 class of words in publications (Genes, Diseases, Mutations, Chemicals, Species) from the PMID. For use cases where you have a large number of results, I provide a script at the end of the document.

Aims:

Determin the Keywords associated to the 14q32 locus evolution in time
Determin journals yielding most citations on the subject

The following data was retreived from Web of science database with the following search therms: TOPIC: ((14q32) OR (DLK1-DIO3)) AND TOPIC: (heart OR cardi) The results were exported as plain text files.

pacman::p_load(bibliometrix,
               pubmed.mineR,
               rio,
               ttutils,
               tidyverse,
               ggplot2,
               ggrepel,
               scholar,
               reshape2)

Overview of the results

f <- c("data_litterature_14q32/14q32_OR_DLK1-DIO3_AND_heartORcardio.txt")
M <- convert2df(file = f, dbsource = "wos", format = "plaintext")

## 
## Converting your wos collection into a bibliographic dataframe
## 
## Done!
## 
## 
## Generating affiliation field tag AU_UN from C1:  Done!

results <- biblioAnalysis(M, sep = ";")
plot(x = results, k = 10, pause = FALSE)

Co-occurence of Author Keywords

# Create keyword co-occurrences network
NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "author_keywords", sep = ";")

# Plot the network
net=networkPlot(NetMatrix, normalize="association", weighted=T, n = 30, Title = "Keyword Co-occurrences", type = "auto", size=T,edgesize = 5,labelsize=0.7)

Genes and diseases analysis

Keywords are quite uninforamtive. I find that a better option is to use gene names and diseases identified by the NCBI Deep learning algorythm pubtator.

Pubtator query

Pubator returns all identifiers merged from a PMID list. Here we want this information for each articles so we need to do this iteratively.

# pubtator_o <- list()
# for (i in 1:length(M$PM)) {
#   pubtator_o[[i]] <- pubtator_function(M$PM[i])
#   if (isInteger(i/100)) {
#     print(i)
#   }
# }
# export(pubtator_o, "data_litterature_14q32/14q32_OR_DLK1-DIO3_AND_heartORcardio_pubtator.rds")

pubtator_o <- import("data_litterature_14q32/14q32_OR_DLK1-DIO3_AND_heartORcardio_pubtator.rds")

Network analysis of genes

Text needs to be reformated to be processed with the bibliometrix package

do <- as.data.frame(do.call(rbind, lapply(pubtator_o, as.character)), stringsAsFactors = F)
colnames(do) <- names(pubtator_o[[1]])
do <- do %>% replace(.=="NULL", NA)
do <- do[do$PMID != " No Data ",]

do <- apply(do,2,function(x) gsub("c[(]", "", x))
do <- apply(do,2,function(x) gsub("[)]", "", x))
do <- as.data.frame(apply(do, 2,function(x) gsub("\"", "", x)), stringsAsFactors = F)
do <- as.data.frame(apply(do, 2,function(x) gsub(">[0-9]+","", x)), stringsAsFactors = F)
do <- as.data.frame(apply(do, 2,function(x) gsub(">MESH:[A-Z][0-9]+","", x)), stringsAsFactors = F)
do <- as.data.frame(apply(do, 2,function(x) gsub(">-","", x)), stringsAsFactors = F)
do <- as.data.frame(apply(do, 2,function(x) gsub(",",";", x)), stringsAsFactors = F)

Now we can replace Keywords with the columns for Genes and/or Diseases from Pubtator

M2 <- M # new df to replace ID
df <- merge(M, do, by.x="PM", by.y = "PMID", all.x = T)


change_ID <- function(new_ID, DF) {
  # remove duplicated gene names (some are capitalized and others not)
  DF$ID <- new_ID
  DF$ID <- gsub("NA","",DF$ID)
  t <- split(toupper(DF$ID), seq(length(DF$ID)))
  t <- lapply(t, function(x) unique(str_trim(strsplit(x, ";")[[1]])))
  t <- lapply(t, function(x) paste(x, collapse = ";"))
  DF$ID <- unlist(t)
  DF$ID <- gsub("NULL","",DF$ID)
  return(DF)
}

new_ID <- paste0(df$Genes) # replacing ID with genes from Pubtator
M2 <- change_ID(new_ID,M2)

##### These other functions may also be helpful.
# summary(results2)
# CS <- conceptualStructure(M2,field="ID", method="CA", minDegree=4, clust=5, stemming=FALSE, labelsize=10, documents=10)

# rio::export(M2, "data_litterature_14q32/14q32_lit.xlsx") # to import in shiny
# biblioshiny()

NetMatrix <- biblioNetwork(M2, analysis = "co-occurrences", network = "keywords", sep = ";")

net=networkPlot(NetMatrix, normalize="association", weighted=T, n = 30, Title = "Keyword Cooccurrences", type = "auto", size=T,edgesize = 5)

Variation of investigated genes over time

ID_evol_p <- function(DF, top_n_ID) {
  KWG_df <- KeywordGrowth(DF, Tag = "ID", sep = ";", top = top_n_ID, cdf = TRUE)
  m <- melt(KWG_df,  measure.vars = c(colnames(KWG_df)[2:(top_n_ID+1)]))
  m <- m %>% mutate(label = if_else(Year == max(Year), as.character(variable), NA_character_))
  
  labelInfo <-
    split(m, m$variable) %>%
    lapply(function(t) {
      data.frame(
        predAtMax = loess(value ~ Year, data = t) %>%
          predict(newdata = data.frame(Year = max(t$Year)))
        , max = max(t$Year)
      )}) %>%
    bind_rows
  
  labelInfo$label = levels(factor(m$variable))
  
  p <- ggplot(m, aes(x=Year, y=value, colour=variable))+
    geom_smooth(se=F) +
    geom_label_repel(data = labelInfo, 
                     aes(x = max, y = predAtMax, 
                         label = label, 
                         color = label),
                     size = 3,
                     nudge_x = 5) +
    theme_minimal() + 
    theme(legend.position = "none")+
    xlim(min(m$Year), 10+max(m$Year))+
    ylab("Cumulative frequency")
  return(p)
}

ID_evol_p(M2,30)

Network analysis of diseases

new_ID <- paste0(df$Diseases) # replacing ID with genes from Pubtator
M2 <- change_ID(new_ID,M2)

NetMatrix <- biblioNetwork(M2, analysis = "co-occurrences", network = "keywords", sep = ";")

net=networkPlot(NetMatrix, normalize="association", weighted=T, n = 50, Title = "Keyword Cooccurrences", type = "auto", size=T,edgesize = 5,labelsize=0.7)

Variation of investigated diseases over time

ID_evol_p(M2,30)

Combined genes and diseases network

new_ID <- paste0(df$Genes, ";",df$Diseases)
M2 <- change_ID(new_ID,M2)
results2 <- biblioAnalysis(M2, sep = ";")
# summary(results2)
# rio::export(M2, "data_litterature_14q32/14q32_lit.xlsx")

NetMatrix <- biblioNetwork(M2, analysis = "co-occurrences", network = "keywords", sep = ";")

net=networkPlot(NetMatrix, normalize="association", weighted=T, n = 50, Title = "Keyword Cooccurrences", type = "auto", size=T,edgesize = 5,labelsize=0.7)

library("wordcloud")
set.seed(1234)
d <- as.data.frame(results2$ID)
wordcloud(words = d$Tab, freq = d$Freq, min.freq = 2,
          max.words=200, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"))

Best journal in topic

number of citation per paper per year
compare to their impact factor

sources <- data.frame(sources = M2$SO , TCperYear = results2$TCperYear)
sources <- sources %>% group_by(sources) %>% summarise(TCpY = sum(TCperYear))
sources$n_aticles <- M2 %>% group_by(SO) %>% count() %>% pull(n)
sources$TCpYpA <- sources$TCpY/sources$n_aticles


impact <- get_impactfactor(journals=sources$sources, max.distance = 0.1)
sources <- merge(sources, impact, by.x="sources", by.y = "Journal", all.x = T)
sources <- sources[!duplicated(sources$sources),]
top_J <- head(sources[order(sources$TCpYpA, decreasing = T),], 15)

ggplot(top_J, aes(y=reorder(sources,TCpYpA),x=TCpYpA))+
  geom_bar(stat = "identity") +
  theme_minimal()+
  theme(legend.position = "none")+
  xlab("Citations/(Years*Articles)")+
  ylab("Journal")

ggplot(top_J, aes(x = ImpactFactor, y=TCpYpA, label=sources, colour=sources))+
  geom_point() +
  geom_label_repel(size = 2.3)+
  scale_y_log10() +
  scale_x_log10() +
  theme_minimal() +
  theme(legend.position = "none") +
  ylab("Citations/(Years*Articles)")

Large queries analyses

##### Function to get citation count from PMID 
##### (modified from RISmed package to avoid errors from multiple queries)
f <- function(id){
  
  base <- "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?retmode=xml&dbfrom=pubmed&id=ID&cmd=neighbor"
  base <- sub("ID", id, base)
  
  the_url <- url(description = base)
  on.exit(close(the_url))
  
  lines <- readLines(the_url)
  
  citedstart <- grep("pubmed_pubmed_citedin", lines)
  
  if(length(citedstart) == 0){
    return(0)
  }
  else{
    citedend <- grep("pubmed_pubmed", lines)
    citedend <- min(citedend[citedend > citedstart])
    
    tags <- grep("<Id>", lines)
    tags <- tags[tags > citedstart & tags < citedend]
    
    if(any(tags)){
      hits <- sub("(.*<ID>)([0-9]+)(.*)","\\2",lines[tags])
      hits <- unique(hits)
      length(hits[hits != id])
    }
    else{
      0
    }   
  }
}

# get 
library(pubmedR)
query <- "atrial fibrillation[Title/Abstract]"
res <- pmQueryTotalCount(query = query, api_key = NULL)
D <- pmApiRequest(query = query, limit = 100000, api_key = NULL)
M <- pmApi2df(D)


library(ttutils)
v <- c()
for (i in 1:length(M$PMID)) {
  v <- c(v,f(M$PMID[i])) # get citations 1 by one with sleep to avoid errors
  Sys.sleep(0.3)
  if (isInteger(i/100)) { # follow the long process
    print(i)
  }
}

# get Genes, Diseases, Mutations, Chemicals, Species from pubtator
library(pubmed.mineR)
pubtator_o <- list()
for (i in 1:length(M$PMID[1:100])) {
  pubtator_o[[i]] <- pubtator_function(M$PMID[i])
  if (isInteger(i/100)) {
    print(i)
  }
}

sessionInfo()

## R version 3.6.1 (2019-07-05)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 7 x64 (build 7601) Service Pack 1
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=French_Canada.1252  LC_CTYPE=French_Canada.1252   
## [3] LC_MONETARY=French_Canada.1252 LC_NUMERIC=C                  
## [5] LC_TIME=French_Canada.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] wordcloud_2.6       RColorBrewer_1.1-2  reshape2_1.4.4     
##  [4] scholar_0.1.7       ggrepel_0.8.2       forcats_0.5.0      
##  [7] stringr_1.4.0       dplyr_1.0.0         purrr_0.3.4        
## [10] readr_1.3.1         tidyr_1.0.2         tibble_3.0.0       
## [13] ggplot2_3.3.1       tidyverse_1.3.0     ttutils_1.0-1      
## [16] rio_0.5.16          pubmed.mineR_1.0.16 bibliometrix_3.0.2 
## 
## loaded via a namespace (and not attached):
##   [1] colorspace_1.4-1     ellipsis_0.3.1       R2HTML_2.3.2        
##   [4] fs_1.4.1             rstudioapi_0.11      dimensionsR_0.0.1   
##   [7] farver_2.0.3         graphlayouts_0.7.0   rscopus_0.6.6       
##  [10] SnowballC_0.7.0      DT_0.13              fansi_0.4.1         
##  [13] lubridate_1.7.8      xml2_1.3.0           splines_3.6.1       
##  [16] codetools_0.2-16     R.methodsS3_1.8.0    leaps_3.1           
##  [19] knitr_1.28           shinythemes_1.1.2    polyclip_1.10-0     
##  [22] jsonlite_1.6.1       broom_0.5.6          cluster_2.1.0       
##  [25] dbplyr_1.4.4         R.oo_1.23.0          ggforce_0.3.1       
##  [28] shiny_1.4.0.2        rentrez_1.2.2        compiler_3.6.1      
##  [31] httr_1.4.1           backports_1.1.6      assertthat_0.2.1    
##  [34] Matrix_1.2-18        fastmap_1.0.1        cli_2.0.2           
##  [37] later_1.0.0          tweenr_1.0.1         htmltools_0.4.0     
##  [40] tools_3.6.1          igraph_1.2.5         gtable_0.3.0        
##  [43] glue_1.4.1           FactoMineR_2.3       Rcpp_1.0.4.6        
##  [46] cellranger_1.1.0     vctrs_0.3.0          nlme_3.1-147        
##  [49] blogdown_0.20        ggraph_2.0.2         xfun_0.14           
##  [52] networkD3_0.4        openxlsx_4.1.4       rvest_0.3.5         
##  [55] mime_0.9             lifecycle_0.2.0      pacman_0.5.1        
##  [58] shinycssloaders_0.3  XML_3.99-0.3         stringdist_0.9.6    
##  [61] factoextra_1.0.7     MASS_7.3-51.5        scales_1.1.1        
##  [64] tidygraph_1.1.2      hms_0.5.3            promises_1.1.0      
##  [67] parallel_3.6.1       yaml_2.2.1           curl_4.3            
##  [70] gridExtra_2.3        stringi_1.4.6        boot_1.3-25         
##  [73] zip_2.0.4            rlang_0.4.6          pkgconfig_2.0.3     
##  [76] bitops_1.0-6         evaluate_0.14        lattice_0.20-40     
##  [79] labeling_0.3         htmlwidgets_1.5.1    tidyselect_1.1.0    
##  [82] plyr_1.8.6           magrittr_1.5         bookdown_0.19       
##  [85] R6_2.4.1             generics_0.0.2       DBI_1.1.0           
##  [88] mgcv_1.8-31          withr_2.2.0          pillar_1.4.4        
##  [91] haven_2.2.0          foreign_0.8-76       scatterplot3d_0.3-41
##  [94] RCurl_1.98-1.1       modelr_0.1.8         crayon_1.3.4        
##  [97] rmarkdown_2.2        viridis_0.5.1        grid_3.6.1          
## [100] readxl_1.3.1         data.table_1.12.8    blob_1.2.1          
## [103] reprex_0.3.0         digest_0.6.25        flashClust_1.01-2   
## [106] R.cache_0.14.0       xtable_1.8-4         httpuv_1.5.2        
## [109] R.utils_2.9.2        munsell_0.5.0        viridisLite_0.3.0   
## [112] pubmedR_0.0.3

How I built this website

Mon, 29 Jun 2020 00:00:00 +0000

Why this post

As a first post, it seemed appropriate to document the process of building this website and regroup resources for future use and help some other users. I entertained the idea of a personal website since I started my graduate studies in bioinformatics to showcase my work, but also build a platform that will extend outside the frame of my thesis. Using RMarkdown as my main work document, the package blogdown is the perfect fit (almost).

Resources I used

Videos

This video (and others in this series) shows how to get your site running on netlify for free!
This video goes trough the customization options of the academic theme.

Web resources

blogdown R book details most steps to get you going.
Academic details how to customize this Hugo theme within the parameters they have made easy to modify.

There are many themes available, from complex to simple on Hugo site. The current theme is Academic, a complex theme. It conveniently has many templates and functions that allow referencing publications, projects or posts and sharing them. The downside of it’s complexity is that customizing it is difficult. One feature I tryed (and failed) to change is allowing a TOC to be displayed with posts rendered from .Rmd or .RMarkdown files.

I found a solution for .md files here. So for now, I simply set toc: true if the file is .md and toc: false if it is a .Rmd since the <root dir>/layouts/_default/single.htm has to be the same for both files.

{{- define "main" -}}
{{ if .Params.toc }}
<div class="container-fluid docs">
    <div class="row flex-xl-nowrap">
      <div class="d-none d-xl-block col-xl-2 docs-toc">
        <ul class="nav toc-top">
          <li><a href="#" id="back_to_top" class="docs-toc-title">{{ i18n "on_this_page" }}</a></li>
        </ul>
        {{ .TableOfContents }}
        {{ partial "docs_toc_foot" . }}
      </div>
      <main class="col-12 col-md-0 col-xl-10 py-md-3 pl-md-5 docs-content" role="main">
{{ end }}
        <article class="article">
            {{ partial "page_header" . }}
            <div class="article-container">
              <div class="article-style">
                {{ .Content }}
              </div>
              {{ partial "page_footer" . }}
            </div>
        </article>
  {{ if .Params.toc }}
      </main>
    </div>
  </div>
  {{ end }}
{{- end -}}

The few issues I had (that may save you time)

Change the URL in <root dir>/config.toml for the site after netlify is running to have sharable posts working.
Create new posts, courses or add publications with the addin button of Rstudio.
If you have a lot of changes (new files) to commit, git may crash when you try to stage them in Rstudio. I use the Tools>shell… git add . then commit either from Rstudio or from the shell and it should work. If git crashed, you may have to go delete the .git/index.lock by command line (if you are using windows the directory won’t be visible) cd <root dir>/.git/ then rm index.lock.
If you have accents in you path (even before your root dir where the website project is located), git won’t work properly and you may end up with a blank git tab in Rstudio.