# Divergent Characteristics of Biomedical Research across Publication Types: A
Quantitative Analysis on the Aging-related Research

Chenxing Qian  Email<EMAIL_ADDRESS>Corresponding Author  Qingyue Guo  Email:
<EMAIL_ADDRESS>Corresponding Author

###### Abstract

This paper investigates differences in characteristics across publication
types for aging-related genetic research. We utilized bibliometric data for
five model species retrieved from authoritative databases including PubMed.
Publications are classified into types according to PubMed. Results indicate
substantial divergence across publication types in attention paid to aging-
related research, scopes of studied genes, and topical preferences. For
instance, comparative studies and meta-analyses show a greater focus on aging
than validation studies. Reviews concentrate more on cell biology while
clinical studies emphasize translational topics. Publication types also
manifest variations in highly studied genes, like APOE for reviews versus GH1
for clinical studies. Despite differences, top genes like insulin are
universally emphasized. Publication types demonstrate similar levels of
imbalance in research efforts to genes. Differences also exist in
bibliometrics like authorship numbers, citation counts, etc. Publication types
show distinct preferences for journals of certain topical specialties and
scope of readership. Overall, findings showcase distinct characteristics of
publication types in studying aging-related genetics, owing to their unique
nature and objectives. This study is the first endeavor to systematically
depict the inherent structure of a biomedical research field from the
perspective of publication types and provides insights into knowledge
production and evaluation patterns across biomedical communities.

Keywords: Aging, Publication Type, Genes, Comparative Studies, Scientometrics,
Informatics

##  1 Introduction

Research publications serve as a means of conveying knowledge, findings, and
novel topics in the field of biomedical sciences. Researchers across different
communities and cultures rely on prior literature to develop and build upon
existing facts, knowledge, and methodologies to advance scientific
discoveries. It is widely acknowledged that the types of publications used to
embody the fundamentals, motivations, and objectives of upcoming studies vary
across various biomedical communities.

Each publication type in biomedical sciences has its unique nature, purpose,
and evaluation criteria [87]. The variations in the use of scientometric
characteristics (such as citation counts, etc.), the collaboration forms, and
the roles played in scientific communication come with the different
publication types [11, 4]. Besides, there might be rudimentary differences in
the objects studied, which are associated with the epistemological and
ontological assumptions across publication types, as [4] indicated for the
field-level differences. Exploring different publication types can thus shed
light on the production patterns of various biomedical knowledge from a fresh
perspective. Moreover, the dissemination of knowledge through different
publication types may hold different levels of value [11]. Therefore, it is
crucial to investigate the scopes and overlaps of topics covered by varying
publication types to redistribute research efforts toward more valuable areas.
With the increasing volume of biomedical publications, it’s becoming
challenging for small groups and individual researchers to collect related
studies from all aspects [39]. Thus, further investigations on publication
types can guide data mining efforts to improve their efficiency and accuracy.

It is becoming increasingly common to see research revealing the scientometric
trends of a field (from large-scale as physics and small-scale neuroimaging)
leveraging the voluminous available bibliographic metadata. However, to the
best of our knowledge, most existent literature only focused on the
divergences of characteristics of a limited choice of biomedical publication
types, regarding the evidence levels of medical reporting, on a small range of
studies, including some specific clinical subdomains [64, 55, 70, 94, 24].
Additionally, no published literature has focused on the scopes of genes,
which are at the core of biological research due to their indispensable
ontological significance, studied by different publication types.

This article provides a comparative overview of differences regarding most
common publication types in biomedical (especially aging-related) research,
including ordinary research articles, comparative studies, clinical studies,
reviews, meta-analyses, systematic reviews, evaluation studies, validation
studies, etc. In this paper, we hypothesized that the characteristics of
biomedical publications will show the differences regarding the scopes and
preferences on topics and genes in various aspects, across the types of
publications with a focus on aging-related research. We used the systems
categorizing publication types defined by NLM (National Library of Medicine)
as proxies for the forms of publication universal for biomedical studies
(Section 3.2). We performed a quantitative and exploratory analysis of the
bibliometric data retrieved from PubMed (Section 3.2).

There are several key reasons for focusing on aging-related genetic research.
Firstly, aging is a universally recognized and inevitable biological process
that has captured the attention of scientists for decades [56, 53]. As a
result, rich biology has been accumulated in this field for humans and other
species. Secondly, aging is closely linked to many other biological processes
[53, 43, 46, 44], and the development of other scientific fields has
contributed to the advancement of aging research. For instance, the increased
understanding of genetics has led to the identification of links between the
aging process and specific genes [45]. Aging-related studies encompass various
types of publications as they cover a broad scope of medical practices. In
addition, we limited this analysis to genetic research as a way to minimize
research dominated by the spirits of fields other than biomedical sciences.

We hope that the presenting analysis can provide insights into advancing the
understanding of aging-related research and improving the current knowledge
production, dissemination, and communication systems in biomedical sciences.

##  2 Literature review

###  2.1 Explaining the various forms of publication in biomedical sciences

The field of biomedical sciences, after the advent of large-scale
bioinformatics [7, 92] and in silico computational techniques [28, 76],
encompass a broad spectrum of topics, ranging from elucidating molecular
mechanisms, explaining unknown genotype-phenotype correlations, to enhancing
the applicability of medical practices. To date, this field has gathered
resources, ideas, and scholars from diverse communities and organizations,
resulting in significant research output [17, 41, 9]. The biomedical sciences
today are a grand assembly of idiosyncratic cultures, philosophies, and
beliefs.

As biomedical sciences increasingly adopt transdisciplinary knowledge
production patterns [77], it is important to recognize that complexity is not
only a result of interdisciplinary components but also intrinsic to biology
per se, which is at the core of biomedical science [34]. Unlike the "normal
science" of Kuhn, research patterns in biology are complex and shaped by the
ways experimental phenomena are produced and maintained [10]. Boundaries
between studies, such as confirmatory/exploratory and theory-informed/theory-
driven, are hard to establish [86, 61, 21]. [69] summarized the processes of
exploring liminal biological knowledge as "the modern biologist is bound to
divide his world into fragments in which parameters can be defined, quantities
measured, qualities identified." Thus, distinct cultures across research
communities might involve varying topics and objects dependent research
patterns, which may shape the writing styles and formats adopted by
researchers in a particular field. Distinct motivations, objectives, and
backgrounds held by research groups lead to utterly different epistemic
cultures [48, 68]. The different systems of generating, validating, and
developing hypotheses among communities might require various types of
evidence and data, and induce various conclusions even given the identical
results [69, 86, 26, 68]. Moreover, the “halo effects” of previous influential
studies in one domain would shape the organization of ideas and representation
of evidence of upcoming publications [[]pp. 40]hofmann2010, schimel2012.
Lastly, the reviewers during the research evaluation processes may expect
certain regimes of well-structured organizations of manuscripts as the past
acknowledged publications.

The divarication of research patterns can be even amplified during the
publication processes, which involve the representation, dissemination,
evaluation, and selection of knowledge and findings. With the breakdown of
disciplinary boundaries, the exponential increase of scientific publications
[39, 89], and the increasingly inevitable specializing trends across
biomedical subdomains, individuals, organizations, together with the
biomedical communities, had worked on establishing platforms and systems
integrating all structured achievements, findings, and knowledge in all active
fields of biomedical and life sciences [82]. As a result, thousands of new
biomedical journals emerged for the purposes of recording and classifying the
new knowledge recognized and generated by newly admitted biomedical subdomains
[23, 27, 96].

Notably, as the ways of research evaluation by most journals, adapted forms of
peer review forward the establishment of divergent publishing standards. [54,
52]. The reviewers involved are “stakeholders” holding pluralistic epistemic
(and nonepistemic) values considering different facets of a system [[]pp.
55]hackett1997. Biological sciences comprise sui generis decentralized systems
of cultures upon which dispersed research efforts are paid [[]pp.
82]knorr1999. The different opinions, values, and even chasms in the
understanding of critical problems across biomedical subdomains constituted of
researchers holding heterogeneous backgrounds and beliefs, might induce and
entrench discrepancies in evaluation and valuation standards. Over time,
divergent standards for organizing studies across subdomains may be reflected
in academic writings, resulting in scientific publications produced under
distinct patterns and presented in various forms. In addition, reviewers tend
to regularize comments to an acceptable cliché which are often confined to
technical issues such as formative standards and some “compulsory”
modifications, including quantitative analysis and experimental results [66],
as the results of the compromission within the epistemic authorities [[]pp.
54]nightingale2007peer,abrams1991predictive,hackett1997, causing the published
manuscripts in one subdomain to be “averaged” or homogenized, and leading to
the peer review process being a double-edged sword, which could be detrimental
to the emergence and dissemination of innovations [30, 62, 18].

###  2.2 Previous literature on publication types in biomedical sciences

Based on the argument above, the varying consensus on accepting and publishing
new knowledge across dispersed biomedical communities contributes to the
development of subdomain-dependent canonical “study forms” (in which research
patterns and representing forms are both implicated). For example, the
research on the functions of molecular components in a pathway and the
contributors of diseases is often related to the comparative genomic analysis
across conditions [72, 3]. The clinical investigations on the effectiveness
and efficacy of treatments are reported via highly structured and clearly
object-oriented formats requiring details on statistical powers and study
designs [22, 51, 97]. Narrative reviews and systematic reviews (including
meta-analyses) are differently accepted and valued by a varying scope of
biomedical communities [90, 78]. To comprehensively integrate and archive
biomedical information, previous studies have classified biomedical research
into publication types, including case reports, systematic reviews, and
comparative studies, etc [60, 87]. This approach provides a standard framework
for categorizing research paradigms and publishing standards. Each type of
publication is unique in its nature, serves a distinct purpose, and is
evaluated using specific criteria.

Previous studies have investigated the variations in contextual and
bibliometric characteristics across different types of publications in the
biomedical sciences [11, 8, 20, 67]. However, these studies have only
categorized publications into three main types: journal articles, books, and
conference proceedings, which is an inadequate classification, particularly
for biomedical sciences where "journal articles" encompass a heterogeneous set
of studies, including evaluation studies, comparative studies, and reviews.
Similar studies on bibliometric characteristics specific to biomedical
sciences [64, 55, 70, 94, 24] have focused on a limited scope of publication
types, such as case reports, cohort studies, population studies, clinical
trials, meta-analyses, and systematic reviews, associated with clinical and
practical biomedical sciences, for the dissemination of medical knowledge on
different evidence levels. It is worth highlighting that the bibliometric
characteristics and trends of publications that fall under broader scopes of
publication types and subdomains, such as genetic research in the form of
comparative studies, evaluation studies, and reviews, and those related to
biomedical subdomains, have rarely been studied. Therefore, there is an urgent
necessity for further investigations into the differences in characteristics
of publications across various types, because of the plausible correlations to
the cultures and standards of different biomedical communities, as
aforementioned. Furthermore, the awareness of these differences might benefit
in informing the development of more effective acceptance and dissemination
strategies for novel findings and facilitating more comprehensive and accurate
data mining and meta-analyses.

###  2.3 The landscape of aging-related research

Aging, widely recognized as the progressive functional decline (and loss) that
most organisms are involved in, has long been a subject of curiosity and
investigation throughout history [53]. The success of isolating the longevity
mutants of C. elegans [47] ignited the establishment of new research paradigms
of aging by the modern technologies and methodologies of manipulating
molecular and cellular bases. Aging research has now become an active line
encompassing almost all subdomains in biomedical sciences, including genetic,
epigenetic, neurological, biochemical, molecular, and cellular mechanisms of
aging, as well as their influence on aging-related diseases [53, 43, 46, 44].
Aging research holds immense biological and medical significance for numerous
reasons. The investigations into aging will shed light on the biological
fundamentals such as cellular senescence, telomere attritions, and
mitochondrial function [53, 29]. The understanding of these processes can thus
inform the development of preventive medicine, therapies, and treatments in
broader scopes of medical research, such as Alzheimer’s disease, Parkinson’s
disease, cardiovascular diseases, cancer biology, and regenerative medicine
[31]. To date, many disciplines emerged as specializations of aging-related
research on different topics. The importance of aging-related research is also
reflected in the increasing annual entries of publications in aging-related
subdomains [31, 63, 42] horizontal to the increasing biomedical publications.

##  3 Hypotheses, study designs, and methodologies

###  3.1 Hypotheses

The fundamental premise of this paper is that publications represent different
study designs and thus reflect varying characteristics. In this section, we
develop several hypotheses based on previous research and discussions.

Here we provided the first hypothesis:

######  Hypothesis 1.

For one specific biomedical subdomain, researchers will show different levels
of preferences on the types of publications, according to the distinct
purposes, methodologies, and criteria in this subdomain.

We find the first hypothesis to be reasonable, supported by relevant
literature. For instance, [6] contended that comparative analyses performed on
biosystems of different sizes and lifespan scales could enhance the
understanding of aging processes. Meta-analyses and systematic reviews, with
evidence synthesized from multiple studies, possess higher levels of
confidence due to stringent selection criteria [71, 65]. The academic
pressures [93], availability of clinical data on aging processes, stronger
evidence, and greater chances of obtaining citations [64], and the critical
healthcare significance, can prompt the production of aging-related syntheses
and reviews with high levels of evidence.

We can further develop the second hypothesis, based on these claims, that the
topics and genetic entities involved in various publication types differ due
to varying motivations, objectives, epistemic cultures, experimentation and
evaluation procedures, and even different scopes of previous studies to be
referenced. For instance, comparative studies tend to focus on functional
hotspots of aging processes, while systematic reviews and meta-analyses may
report more on genetic entities with clinical significance.

######  Hypothesis 2.

As the objectives, nature, and evaluation of different publication types vary,
there are discrepancies in the scopes of genes (and the corresponding
products) and the biomedical topics across different publication types.

Previous studies have highlighted the disproportionate attention given to a
small subset of human protein-coding genes, which is partly due to historical
and technical factors rather than their biological significance [79, 36, 81].
We contend that this imbalance persists even within a single publication type.
Furthermore, we propose that it may also extend to other biomedical topics, as
different publication types have distinct formats and styles that facilitate
reporting new findings from diverse subdomains. We further posit that the
specific subset of genes that attracts the most attention varies across
publication types due to their distinct nature, objectives, and evaluation
criteria, as we previously mentioned.

######  Hypothesis 3.

Focusing on each publication type, the research attention to genes and
biomedical topics is unbalanced, as embodied in the publication and citation
counts. The genes receiving the most research attention vary across
publication types, corresponding to the nature, objectives, and evaluations of
different publication types.

There is adequate literature examining the bibliometric characteristics
specific to biomedical publications. For example, [49] and [50] investigated
the differences in citation behaviors across medical subject headings (MeSH).
[12] investigated the representation of authorship information in biomedical
publications. [81] explored publishing contexts, authorship, funding, and
citation counts of biomedical publications focused on early-stage or upcoming
genetic research. [24] reported temporal trends in the annual entry of
biomedical publications across different publication types. Similarly, studies
specific to various medical subdomains have also been conducted, such as [94]
and [70]. Furthermore, [64] uncovered differences in citation impact among
various publication types in biomedical research. Hence, it is reasonable to
expect differences in bibliometric behaviors across different publication
types in the biomedical field.

######  Hypothesis 4.

The bibliometric characteristics of publications are divergent based on the
publication types, including publication and citation counts, authorship
information, funding history and resources, and journals appreciated.

###  3.2 Data retrieval

The bibliometric data were retrieved from PubMed. We chose 5 representative
species intensively studied both inside and outside aging-related fields
(human, mouse, rat, fly, and C.elegans) for further analysis. We restricted
our analysis to publications directly related to genetic research and
separated the aging-related and nonaging-related publications by the following
steps. We used curated data from PubTator [88] to filter publication records
only related to genetic research by including publications mentioning the
symbols (or synonyms) of genes in titles and abstracts at least one time. As
the names, symbols, and NCBI IDs are different for genes from different
species, we obtained and separated the biomedical genetic publications on the
5 representative species.

Thereafter, we further classified the publications as "aging-related" by
including the publications tagged by MeSH terms [19] about aging ("Aging",
“Cellular Senescence”, “Erythrocyte Aging”, “Senescence-Associated Secretory
Phenotype”, “Telomere Shortening”, “Cognitive Aging”, “Immunosenescence”,
“Longevity”). The rest of the genetic publications were classified as
"nonaging-related". As the information on publication types of each retrieved
publication was included in PubMed, we further categorized each "aging-
related" and "nonaging-related" publication using a system of publication
types modified from the original NLM-defined publication type classification
rules (Table 1), to assemble analogous publication types and classify
publication types without results and discussions meeting certain specific
biomedical criteria. Detailed procedures on data retrieval were recorded in
Section A.1.

Additionally, to provide a more accurate portrayal of the characteristics of
each publication type, we also counted the citation counts (the total number
of being cited) of "aging-related" and "nonaging-related" publications for
each type, using the iCite databases [40]. The specific procedures for
accessing and collecting these citation records are outlined in Section A.2.

Table 1: Mapping the merged publication categories in this paper to the original categories by PubMed. The merged categories contain studies sharing similar research and bibliometric features.  Merged Categories | Original Categories
---|---
Research Article | Journal Article;
| Corrected and Republished Article
| (excluding the publications that belong to other types)
Clinical Study | Pragmatic Clinical Trial;
| Observational Study, Veterinary;
| Clinical Conference; Clinical Study;
| Clinical Trial, Veterinary;
| Clinical Trial Protocol; Clinical Trial, Phase I;
| Clinical Trial, Phase II; Clinical Trial, Phase III;
| Clinical Trial; Randomized Controlled Trial;
| Case Reports; Controlled Clinical Trial;
| Observational Study;
Meta-Analysis, Systematic Review | Meta-Analysis; Systematic Review
Comment, Letter, News | Comment; News; Letter

##  4 Results

###  4.1 Divergent levels of preferences to aging-related research for various
publication types

To demonstrate the diverging attention paid to aging-related research relative
to nonaging-related research by different publication types, we employed the
RE model (Section A.3) using the publication and citation fractions (Section
A.2) to show the variability of attention to the aging-related field across
publication types and five frequently studied species: Human, Mouse, Rat,
C.elegans and fly (Figure 1, Figure S1 and Figure S2).

As shown in Figure 1 and Figure S2, the average attention to aging-related
research varies among publication types in all 5 species. The large widths of
the funnels especially in subfigures of mouse, human, and rat indicate an
apparent divergence of attention to aging among selected publication types.
The statistical diagnostics indicated the strong divergence of attention,
quantified as the heterogeneity in the RE model, by various publication types
(Table S1). Among the five species, C.elegans and Fly showed more consistency
in the attention across publication types compared to Human, Mouse and Rat.
Considering that C.elegans and Fly are important model animals for aging, they
may have less versatile functions in biomedical research. The divergence of
publication types, which stems from the complexity of biology, is much less
manifested in C.elegans and Fly.

Despite the different levels of heterogeneity, there are similar patterns
between species. From Figure 1, we can observe that the attentions by research
articles and reviews are proximate to the average attention considering all
publication types to aging-related research for these species. In general,
evaluation studies, clinical studies, and validation studies showed less
attention to aging-related research, while the attention by meta-analyses &
systematic reviews, and comparative studies exceed the average attention in
the five species (Figure 1 and Figure S1). In sum, Figure 1 and Figure S1
visually validated Hypothesis 1 and suggested considerable preferences on
aging-related studies by specific comparative studies, clinical studies and
validation studies.

Figure 1: We displayed the fraction of attention by seven important
publication types, including research articles, reviews, comparative studies,
meta-analyses & systematic reviews, evaluation studies, validation studies,
and comments & letters & news, across five species in the funnel plots. The
dashed lines in the middle of the subfigures represent the average attention
to aging for one species. The large widths of the white “funnels” suggest a
strong heterogeneity of attention among the seven publication types. The
$I^{2}$ quantity, which represents the level of heterogeneity, is appended to
the titles of subfigures.

Among the seven publication types, we calculated Cook’s distance, a measure of
heterogeneity contributed by each publication type, in the five species by
removing one publication at each time (Figure 2). For Human, Mouse and Rat,
comparative studies and evaluation studies are major contributors to attention
divergence, suggesting distinct research patterns and focus guided by academic
styles. For C.elegans and Fly, the research articles showed more intensive
attention to aging-related research compared to other publication types.

Figure 2: We displayed the Cook’s distance, which measures the amount of
heterogeneity contributed by each publication type in the RE model, for the
five species by removing each publication type out repeatedly.

###  4.2 Different scopes of aging-related topics and genes were investigated
by various publication types

There exist huge variations in the genes studied and preferences on genes
among various publication types. For example, clinical studies and mechanism
studies show contrasting interests in how and where the genes function. Here
we quantitatively demonstrated the variation of research attention on genes by
several approaches.

In Section 3.1 we proposed the hypothesis on the inconsistent scopes of
research objects (Hypothesis 2) across publication types. Figure 3 partially
validated the distributions and connections of research topics under four
publication forms by clustering the MeSH terms of publication data retrieved
from NCBI (Section 3.2) via VosViewer [83, 84]. Reviews, comparative studies,
clinical studies, and meta-analyses & systematic reviews show distinct
emphasis on ranging from medical care to cell biology. In contrast to reviews
that majorly focus on the signal transduction on cellular senescence, the
comparative studies centered on three more detached subfields: genotypic
studies on aging, gene expression on cellular senescence and human development
biology regarding the age factors during different stages (Figure 3(a) and
3(b)). As a major publication type, the clinical studies even demonstrated a
more bifurcated trend on topics (Figure 3(c)): one class (red and yellow)
consists of studies on clinical progressions of diseases including genotypic
and polymorphism research, the opposite class is composed by studies on the
regulations and impacts of hormones, as well as related kinesiological
studies. Besides genotypic and longevity studies (which are also conveyed via
the proceeding three publication types), meta-analyses and systematic reviews,
usually utilized as approaches synthesizing and integrating widely-sourced
data and conclusion, naturally emphasized genome-wide association studies
(GWAS), cognition decline and hypertension (Figure 3(d)).

(a) Reviews

(b) Comparative studies

(c) Clinical studies

(d) Meta-analyses and systematic reviews

Figure 3: The co-occurrence maps (by VOSviewer) of research topics based on
the texts of titles and abstracts of literature from four publication types.
Each color refers to one cluster based on topical relevance. Figure 4: The
heatmaps showing the overlaps of the scopes of gene studied in different
publication types for Human (A), Mouse (B), Rat ( C), C.elegans(D) and Fly
(E). The elements in heatmaps represent the proportion of the intersection of
genes studied by the two publication types (indexed by the row and the column)
concerning the total genes studied by one publication type (indexed by the
row).

We took a closer look at the scopes of genes studied by different publication
types and then measured the overlaps of the scopes of genes studied in
different publication types as displayed in Figure 4 and Figure S4. There were
differences between the scopes of genes investigated across the selected five
species. For human, 3463 of over 20000 total genes were studied by research
articles, comparative studies, reviews, evaluation studies, meta-analyses &
systematic reviews, compared to the numbers of overlapped genes for the rest 4
species, among which the overlap of scopes of the five publication types in
rat, which is 418 in number, is highest among these four species (Figure S4).

For the five selected species in this study, research articles covered the
vast majority of genes of the species studied for aging-related research
(Figure 4, Figure S4). reviews and comparative studies focused on a large
subset of genes investigated in research articles, which are in 60$\sim$40% in
the number of genes studied by research articles for human and mouse while
30$\sim$10% for C.elegans and Fly. Comparatively, meta-analyses & systematic
reviews, evaluation studies, and validation studies showed a more concentrated
focus on shrinking scopes of genes (Figure 4, Figure S4).

A considerable portion of genes studied by meta-analyses & systematic reviews,
evaluation studies, validation studies, and clinical studies were contained in
the attention scope of reviews and comparative studies, for the four species
except C.elegans (Figure 4, Figure S4).

We also found that the research effort of clinical studies was concentrated on
a large proportion of genes investigated by research articles for human, which
was 52% in proportion, compared to the proportions up to 16% for the remaining
four species (Figure 4). Only a small fraction of genes investigated by
research articles and other publication types were included in twin studies
for human, mouse, rat, and fly (Figure 4). However, a relatively larger
proportion of genes in twin studies were covered by meta-analyses & systematic
reviews (Figure 4).

We also performed a post hoc analysis to figure out whether the divergence of
attention paid to aging-related genetic research across publication types was
contributed by the biological natures of these genes. We employed the RE model
(Section A.3) to aging-related publication fractions on 3463 human genes
within the overlap of research articles, reviews, comparative studies, meta-
analyses & systematic reviews and evaluation studies, compared to the results
without filtering genes (Figure S5). The heterogeneity patterns for
publication types remained to a large extent after selecting the intersected
genes. It is then reasonable to deduce that the publication type-associated
protocols not only gave rise to differing genetic scope but mostly introduced
divergences of attention to aging-related research.

We furthermore took a closer look at the genes preferred to be studied by
showing the ten most frequently studied human genes by various publication
types according to the accumulated publication numbers (Table 2, Table S3).
The publication of the most frequently studied 10 genes will account for a
significant portion (from 20% to 50% varying from publication types) of the
total genetic studies (Figure 5), as is hypothesized in Hypothesis 3 and
further discussed in Section 4.3.

Table 2: The top 10 human genes which were intensively studied in aging-related research according to the aging-related publication counts on these genes across 8 major publication types Rank | Research Article | Review
---|---|---
1 | tumor protein p53 | insulin
2 | insulin | insulin-like growth factor 1
3 | cyclin-dependent kinase inhibitor 2A | tumor protein p53
4 | galactosidase beta 1 | growth hormone 1
5 | apolipoprotein E | amyloid beta precursor protein
6 | interleukin 6 | sirtuin 1
7 | H3 histone pseudogene 16 | mechanistic target of rapamycin kinase
8 | insulin-like growth factor 1 | cyclin-dependent kinase inhibitor 2A
9 | cyclin-dependent kinase inhibitor 1A | microtubule-associated protein tau
10 | tumor necrosis factor | apolipoprotein E
Rank | Comparative Study | Meta-Analysis,Systematic Review
1 | insulin | apolipoprotein E
2 | insulin-like growth factor 1 | forkhead box O3
3 | interleukin 6 | interleukin 6
4 | tumor necrosis factor | insulin
5 | apolipoprotein E | insulin-like growth factor 1
6 | growth hormone 1 | parathyroid hormone
7 | CD4 molecule | CD4 molecule
8 | amyloid beta precursor protein | C-reactive protein
9 | tumor protein p53 | paraoxonase 1
10 | CD8a molecule | CD8a molecule
Rank | Evaluation Study | Validation Study
1 | hemoglobin subunit alpha 1 | anti-Mullerian hormone
2 | insulin | apolipoprotein E
3 | cut like homeobox 1 | insulin-like growth factor 1
4 | selenium binding protein 1 | TNF superfamily member 10
5 | ABL proto-oncogene 1 | poly(ADP-ribose) polymerase 1
6 | anti-Mullerian hormone | cytochrome c oxidase subunit 8A
7 | caspase 3 | C-reactive protein
8 | CD34 molecule | dedicator of cytokinesis 3
9 | D-box binding PAR bZIP TF | FAT atypical cadherin 1
10 | MYC proto-oncogene | microtubule-associated protein tau
Rank | Twin Study | Clinical Study
1 | apolipoprotein E | insulin
2 | insulin | insulin-like growth factor 1
3 | interleukin 6 | growth hormone 1
4 | apolipoprotein A1 | interleukin 6
5 | apolipoprotein B | C-reactive protein
6 | C-reactive protein | tumor necrosis factor
7 | angiotensin I converting enzyme | CD59 molecule (CD59 blood group)
8 | tumor necrosis factor | growth hormone releasing hormone
9 | E1A binding protein p300 | apolipoprotein E
10 | insulin like growth factor 2 | parathyroid hormone

Despite the resemblance between research articles and reviews, research
articles put extra concentration on genes that are more studied in the
fundamental context of biology on molecular mechanisms of cellular aging
(Table 2), for instance, galactosidase beta 1 (BETA1), H3 histone pseudogene
16 (H3H16P), and cyclin-dependent kinase inhibitor 1A (CDK-inhibitor 1A).
Evaluation studies and validation studies show distinctive studying
preferences with little overlap with other publications (Table 2). However,
this substantial shift in evaluation studies became weaker using citation
counts (Table S3). One plausible explanation is that the total publication
counts of these two publication types are very limited while citation counts
are fair enough to avoid statistical instability (Section A.1 and A.2). As
another major publication type, clinical studies concentrated on genes that
have higher clinical significance and are easy to detect in surgery and
clinical practice, including multiple kinds of hormones, such as insulin,
growth hormone 1 (GH1), growth hormone-releasing hormone (GHRH), or biomarkers
like C-reactive protein (CRP).

Although shared a large overlap with other publication types, meta-analyses &
systematic reviews especially had frequently studied genes uncovered by the
predominantly investigated genes in other publication types, such as CD4 and
CD8a, which play important roles in age-related maladies and disorders,
forkhead box O3 (FOXO3) and parathyroid hormone (PTH). Twin Study also had
several genes not listed by others, including apolipoprotein B (ApoB),
apolipoprotein A1 (ApoA1), angiotensin I converting enzyme (ACE), insulin
growth factor 2 (IGF2), and E1A binding protein p300 (p300) which could be
measured directly from serum.

Though differences between publication types exist, it was notable that some
genes were put enough emphasis on by all publication types. For example, the
growth-associated genes coding the insulin and the insulin-like growth factor
(IGF) kept the most popular positions across the selected eight publication
types. Research articles and reviews showed a strong resemblance between
preferred areas. The Apolipoprotein E (ApoE) which is associated with lipid
metabolism and Alzheimer’s disease draws the most intensive attention by all
publication types except evaluation studies. These most frequently studied
genes are involved with multiple aging-related topics including metabolism,
cellular pathways, oncology, development, and immunology. Publication types
showed varying interests in human genes and the aging-related topics behind
them (Table 2).

###  4.3 The imbalance of attention on genes by each publication type

As suggested by recent research that there exists a highly imbalanced research
effort prevailing in biomedical research to individual human protein-coding
genes [79, 36, 81], we hypothesized on the imbalance of bibliometric data on
biomedical topics across types of publications (Hypothesis 3).

Figure 5: Cumulative share of aging-related literature on human genes tagged
in the filtered publications from PubMed for the four most common publication
types (research articles, reviews, comparative studies, and clinical studies).
Gene rank refers to the order of human genes according to the associated
cumulative publication numbers. The gene with the most publication equivalents
would have ranked 1. The share of total literature contributed by top 25%,
2.5%, and 10 genes are shaded. Note: for the fourth sub-figure the share of
top ten genes is not shaded because the number 10 exceeds the number of 2.5%
of total genes involved.

Partially supporting our hypothesis, the imbalance of research efforts among
four major types is observed (Figure 5), as well as the topics and genes
preferred to be unevenly investigated for each type (Figure 3 and Table 1).
However, counter-intuitively, we cannot observe obvious differing patterns in
the distribution of research attention across types from Figure 5, because it
is reasonable to assume a larger extent of imbalance for reviews, comparative
studies, and clinical studies as reviews and application studies should be
developed upon the discovery by liminal research studies. It may suggest a
complexity in the interchange and dissemination of knowledge between types in
this specific field, which will be further developed in Section 5.

###  4.4 Bibliometric variations among publication types further characterize
the intricacies

By Hypothesis 4 we may infer the divergence in the bibliometric dimensions of
the publication data. In this subsection, we provide data supporting our
hypothesis and argue that given bibliometric metrics among publication types,
we can further characterize the roles and functions of specific types in
knowledge dissemination.

We first argue that aging-related publications demonstrate certain patterns of
preferences in some journals regarding or regardless of the publication types
(Table 3 and Figure S6). The top 10 journals ranked by aging-related
publication counts for each publication type are exhibited first (Table 3), as
well as the top 10 journals with the highest proportions on aging-related
studies among 8 publication types with Bnferroni-corrected p-values (Table
S4).

Rank | Research Article | Count | Review | Count
---|---|---|---|---
1 | PloS one | 379 | Ageing research reviews | 90
2 | Neurobiology of aging | 286 | IJMS | 84
3 | Aging cell | 278 | Experimental gerontology | 80
4 | Aging | 275 | Mechanisms of ageing and development | 72
5 | Mechanisms of ageing and development | 205 | Annals of the New York Academy of Sciences | 48
6 | JCEM | 205 | Cells | 42
7 | Scientific reports | 194 | Aging | 33
8 | Experimental gerontology | 188 | Advances in experimental medicine and biology | 29
9 | PNAS | 157 | Nihon rinsho. Japanese J. of clinical med. | 29
10 | BBRC | 155 | Gerontology | 26
Rank | Comparative Study | Count | Meta-Analysis & Systematic Review | Count
0 | JCEM | 70 | Ageing research reviews | 10
1 | Neurobiology of aging | 50 | Experimental gerontology | 7
2 | The journals of gerontology. Series A | 46 | PloS one | 6
3 | J. of immunology (Baltimore) | 43 | Rejuvenation research | 6
4 | Mechanisms of ageing and development | 42 | The journals of gerontology. Series A | 4
5 | Experimental gerontology | 33 | Aging cell | 4
6 | Age (Dordrecht, Netherlands) | 30 | Zeitschrift fur Gerontologie und Geriatrie | 3
7 | Blood | 21 | Mechanisms of ageing and development | 3
8 | Brain research | 21 | Human reproduction update | 3
9 | Clinical chemistry | 20 | Nature communications | 3
Rank | Evaluation Study | Count | Validation Study | Count
1 | Aging clinical and experimental research | 2 | The J. of nutritional biochemistry | 3
2 | The American J. of surgical pathology | 2 | BioMed research international | 2
3 | Quality of life research | 2 | Rejuvenation research | 2
4 | Acta diabetologica | 1 | PloS one | 2
5 | Pigment cell & melanoma research | 1 | J. of applied physiology | 2
6 | Klinika oczna | 1 | Molecular immunology | 2
7 | Maturitas | 1 | Acta neuropathologica communications | 1
8 | Medicina (Kaunas, Lithuania) | 1 | Medicina (Kaunas, Lithuania) | 1
9 | NMR in biomedicine | 1 | JCEM | 1
10 | Neuroscience letters | 1 | The American J. of pathology | 1
Rank | Twin Study | Count | Clinical Study | Count
1 | JECM | 7 | JECM | 130
2 | Aging | 2 | Metabolism: clinical and experimental | 28
3 | Diabetologia | 2 | European J. of endocrinology | 28
4 | The journals of gerontology. Series A | 2 | PloS one | 25
5 | The J. of experimental medicine | 2 | Neurobiology of aging | 23
6 | PloS one | 2 | J. of applied physiology | 18
7 | Neuropsychologia | 2 | Mechanisms of ageing and development | 17
8 | Nature | 2 | Hormone research | 17
9 | Neurology | 2 | The American J. of clinical nutrition | 16
10 | American J. of medical genetics. Part B | 2 | The journals of gerontology. Series A | 15

Table 3: The top 10 journals with the highest publication counts across 8
different publication types. BBRC: Biochemical and Biophysical Research
Communications; PNAS: Proceedings of the National Academy of Sciences of the
United States of America; IJMS: International J. of Molecular Sciences; JCEM:
The J. of Clinical Endocrinology and Metabolism

From the perspective of subject specialty and audience scope of journals,
different article types match journals of various natures. Research articles
tend to be published in high-impact multidisciplinary journals, such as PLoS
One and PNAS, and are also seen in aging-related specialized journals, such as
Experimental Gerontology. In contrast, reviews prefer more specialized
journals (and some journals specializing in reviews), such as Mechanisms of
Ageing and Development, and are also present in high-impact interdisciplinary
journals, such as IJMS. Comparative studies mainly target specialized medical
journals, with some present in general journals. Except for those 6
publications on PloS one, meta-analyses and systematic reviews are only
published in medical journals specializing in particular domains, such as
Rejuvenation Research and Experimental Gerontology. Evaluation studies and
validation studies are limited to specialized and topical medical
publications. Twin studies and clinical studies have a relatively wider
distribution and are also present in general science journals.

In addition, specialized journals concentrate on specific research issues,
while general journals cover a wider range of topics. Research articles cover
a wide range of topics, from fundamental landscapes such as cell biology and
genetics to clinical research such as pharmacology and pathology. Reviews
concentrate on gerontology themes, such as mechanisms of aging and anti-aging
interventions, with some clinical reviews included. In contrast, comparative
studies focus on disease models and mechanisms. Evaluation studies are
concerned with quality assessment and psychometric research. Validation
studies focus on technique and methodology validation. Twin studies are
primarily in the field of genetics. Clinical studies mainly involve clinical
trials and treatments.

In addition, the four major publication types (research articles, reviews,
comparative studies, and clinical studies) manifested different patterns and
trends in commonly used scientometric measures (Figure 6), as well as for the
other five minor types as shown in Figure 7.

Figure 6: The average values of five frequently used bibliometric measures for
aging-related research articles, reviews, comparative studies, and clinical
studies on human genes entered from 1991 to 2020. The borders of shaded areas
indicated the 95% confidence intervals of estimation by bootstrap.

Except for clinical studies and reviews, the number of authors for the other
two publication types showed an overall upward trend, with the author number
for research articles steadily increasing (Figure 6). Over the three decades
from 1991-2020, the average number of genes studied per article for clinical
studies declined, while the other three publication types remained relatively
stable (Figure 6). The length of titles and abstracts showed slightly
increasing trends for four publication types. The average annual citation
increment and average reference counts also increased for all types (Figure
6). Among these four major types, reviews had the fastest growth in citation
count, while the other three types grew at comparable levels (Figure 6).
Unlike hotspot research areas, reviews had the fewest authors, followed by
research articles, while comparative studies and clinical studies had the most
authors (Figure 6). Reviews contained the greatest number of references, with
the other three types growing at similar speeds. For reviews, both the mean
yearly citation increment and average citation count underwent rapid growth
around 2008 (Figure 6).

Figure 7: The average values of four frequently used bibliometric measures for
aging-related meta-analyses & systematic reviews, evaluation studies,
validation studies, twin studies, and comments & news & letters on human genes
entered from 1991 to 2020.

The average length of abstracts and titles showed a steady increase for all
five minor publication types (Figure 7). Meta-analyses & systematic reviews,
compared to the other four minor article types, showed a significant rise in
the average number of authors over three decades (Figure 7). Also, meta-
analyses & systematic reviews received more average annual citation increments
compared to the other four minor types, with the average annual citation
counts increase for validation studies rising after 2010 (Figure 7). The
average reference counts also increased over three decades for both meta-
analyses & systematic reviews and twin studies (Figure 7).

We also performed a sensitivity analysis to inspect how the aging-related
research fractions changed over the past four decades. As shown in Figure 8,
we found there are substantial differences in the temporal trends of attention
to aging-related research for various publication types. Research articles,
comparative studies, reviews, and clinical studies remained stable on the
fractions of aging-related publications over 1980-2020 across five selected
species (the seemingly wiggling trends of Rat, Fly, and C.elegans is due to
the limited sample sizes). Meta-analyses & systematic reviews, twin studies,
evaluation studies, and validation studies on aging were initiated during the
1990s and the aging-related fractions of these four publication types evolved
to stable status thereafter.

Figure 8: The temporal trends of the publication fractions of aging-related
research by 8 publication types (research articles, reviews, comparative
studies, meta-Analyses & systematic reviews, evaluation studies, validation
studies, twin studies, and clinical studies) from 1980 to 2020. The
publication fractions were calculated by retrieving the recorded publications
from the PubMed data sets.

The growth rate of the number of papers in different publication types can
somewhat reveal the current focus of development in a field. Compared to the
around three times growth of the annual publication number of research
articles, the rapid growth in the annual publication number of meta-analyses
and systematic and the fluctuations of annual publication counts for the rest
six types in aging-related literature is worth concern (Figure 8, Table S2,
Figure S3). Both meta-analyses and systematic reviews are powerful in
assessing the correctness and effectiveness of theories and treatments by
synthesizing studies and identifying trends unapparent in individual studies
[74], and thus contain higher levels of evidence with more stringent
statistical thresholds and fewer possibilities of being false positive. The
increasing trend of meta-analyses and systematic reviews may indicate a higher
acceptance of research with higher levels of evidence for the academic
communities. It was also notable that the annual publication numbers of
comparative studies and clinical studies expanded less rapidly than research
articles and reviews for the human and the mouse (Figure S2).

##  5 Discussion and Conclusion

This paper investigated and compared attention in the aging-related domain on
genes across 12 publication types in 5 species. Using the fraction of aging-
related publications and citation counts for each gene, we quantified the
attention to aging-related research relative to nonaging-related research by
applying a RE model. Moreover, we investigated the scopes, overlaps, and
inequality of research efforts of genes studied by various publication types
for aging-related research. This study aimed to indicate diverse genetic
research interests impacted by different publication protocols in the context
of aging-related research.

###  5.1 Publication types serve for naturally different purposes and
investigate intertwining scopes

The exhibited data indicates naturally different aims for each type from
several perspectives. First, each type pays uneven attention to one particular
field. It was commonly acknowledged that the variations of attention to a
particular field vary across different publication types. Consistent with
previous investigations [74], we demonstrated that the meta-analyses and
systematic reviews focused more on aging-related research on genes (Figure 1).
Additionally, the comparative studies showed the richest attention to aging-
related genetic research (Figure 1) among seven publication types (Figure 2).
The characteristic of comparative studies covering multiple species and
lifespan intervals in aging-related fields [6] might have contributed to the
preference of this publication type by researchers. Interestingly, attention
to aging-related genetic research was the lowest in evaluation studies
compared to other publication types (Figure 1, Figure 2).

We further examined the potential contributors to the diverging aging-related
attention by comparing the average aging-related research fractions across
publication types and species before and after performing the RE model
(Section A.3) on the overlap of genetic scopes among five publication types
(Figure S5). It was plausible to infer from the confirmatory results in Figure
S5 that the various publication type-associated factors (such as the protocols
conducting research), rather than the different biological nature of genes
concentrated on by different publication types, resulted in the inconsistency
of aging-related attention.

Secondly, the differences and similarities between the genetic scopes in
various publication types help us understand how the publication type-
associated factors influence the preferences of genes. Figure 3 suggests
methodological preferences and object-level variation among four types of
publications, from genotypic technologies to clinical investigations. Table 2
provides further evidence that these types concentrate on different branches
of biomedical sciences ranging from the context of cellular fundamentals to
clinical hormones studies. Furthermore, the frequencies of publications in
Table 3 signify various scopes and levels of readerships and specialization of
journals among types of publications.

Different aging-related genetic topics received varying levels of attention
across publication types. From Table 2 and Table S3, we can observe that the
eight displayed publication types focused on some common topics, including the
regulation (insulin and IGF1) and immunology (interleukin 6) of aging
processes [32, 5, 57]. The ApoE which involved neurodegenerative and
cardiovascular diseases [95] received the richest level of concern among all
publication types. The research articles put more emphasis on CDK-inhibitor 1A
and 2A, p53, and BETA1 which are associated with the molecular mechanisms and
identification of cellular senescence [14, 37]. The CD4 and CD8a involved in
the immunological responsiveness to carcinogenic cells and exogenous [35]
components were intensively focused on by comparative studies, meta-analyses,
and systematic reviews. Consistent with the methodologies and objectives of
evaluation studies and validation studies which aim to evaluate and benchmark
the effectiveness of procedures, these two types of studies focus more on the
aging biomarkers whose levels reflect multiple aspects of aging processes,
such as caspase 3 [2], CD34 [25] and CRP [58]. Numerous studies of these two
types were also involved with genetic markers of several classes of aging. For
example, the changes in levels of the hemoglobin subunit alpha 1, anti-
Mullerian hormone, and selenium binding protein 1 are associated with various
age-related diseases [15, 91]. The twin studies concentrated more on molecules
that are accessible by clinical techniques and lead to or suggest severe
diseases. More concretely, ApoB, ApoA1, p300, and ACE are associated with age-
related cardiovascular diseases, cancers, and cognitive decline [13, 75].
Remarkably, the genes received unbalanced citation frequencies depending on
the topics. Comparing Table 2 and Table S3, we can observe that clearly
insulin, IGF1, and interleukin 6 impacts the upcoming studies
disproportionately compared to the publication data. The imbalance is
conspicuous for evaluation studies. This evidence might suggest that the
historical reasons such as the landmarking impacts inspired and promoted by
the research on insulin, IGF1, and interleukin 6 exerted disproportionate
influence compared to its actual importance on these genes in the evaluation
studies.

Third, the different topology between scopes of genes investigated across
species may suggest dissimilar research patterns (Figure 4). We observed that
half aging-related genes studied in the research articles were included in
clinical studies, while the percentage for the rest four species was under 20%
(Figure 4). This is consistent with the objectives of clinical studies that
aim to improve the treatments and interventions for human healthcare. We also
found an interesting result: the divergent attention genetic scopes of
comparative studies and reviews across five species, suggested by the
relatively lower percentages of genes studied in comparative studies also
investigated in reviews (Figure 4). This may suggest barriers and less smooth
knowledge diffusion between reviews and comparative studies.

###  5.2 Complex steering forces behind knowledge production and dissemination

The data from Figure 5 in Section 4.3 suggests the hidden complexity of
knowledge dissemination and production processes among publication types, as
embodied by the similar levels of imbalance of attention. Here we closely
examine the assumption that reviews and application studies (such as
comparative studies and clinical studies) are built upon original studies
(such as research articles) and thus are more imbalanced into targeted topics
with special significance. From Figure 9 we can see that the counts of aging-
related reviews on each gene are highly correlated with both aging-related
journal articles (including all articles via journals) and nonaging-related
reviews. Therefore, we may argue that aging-related reviews are to some extent
developed by referencing the boarder scope of aging-related studies.
Nevertheless, the correlation between aging-related and nonaging-related
journal articles and reviews in Figure 5, together with the similar levels of
imbalance in Figure 5, implies more complicated contributors to knowledge
production and dissemination in biomedical sciences. Knowledge production in
biomedical science, which is at the core of scientific communication, may not
only be dependent on natural and scientific significance.

Figure 9: Scatter plots of the publication counts of aging-related review
counts v.s. nonaging-related review counts, aging-related journal article
counts v.s. nonaging-related journal article counts, and aging-related journal
article counts v.s. aging-related review counts. Pearson’s, Spearman’s, and
Kendall’s correlations are labeled.

###  5.3 In sum: a finer characterization of knowledge production and
dissemination in a subdomain

In this article, we performed a comprehensive quantitative analysis of the
publication data of aging-related subdomains regarding different publication
types. Our data signifies different patterns of research attention and
overlapping scopes of biomedical topics. Furthermore, we reproduced the
scientometric data and investigated the imbalance regime of research efforts
across publication types. By applying various quantitative methods and
examining data on several dimensions, our analysis is embodied to convey a
finer characterization of the roles and functions of each publication type in
the biomedical knowledge production systems.

This study has several limitations. To begin with, although we adopted the
advocated MeSH-based search strategy [50, 49], not all publications recorded
were tagged with appropriate labels summarizing the information of publication
types by MeSH. In addition, some of the current publications were not indexed
by PubMed and thus excluded from this study. The vague boundaries among the
publication types, such as comparative studies and clinical studies (including
the original MeSH categories), are worth noting. Also, there is the
possibility that articles were manually tagged as the adjacent publication
types, for example, one systematic review may be tagged as “Review”. In this
study, we quantify the attention to aging-related research using the RE model,
which requires normality (or close normality) because of its statistical
nature. However, we observed the long-tailed distribution of the publication
and citation fractions on genes, which was inconsistent with the model
assumptions.

We depicted the attention patterns in biomedical research of diverse
publication types using publication information from PubMed. The elevation of
large-scale bibliometric data provides a basis for a thorough and objective
understanding of attention in one field. The enhanced understanding of the
attention pattern differences between publication types should be able to help
promote scientific communication across communities holding varying research
ideologies and methodologies. Moreover, the presenting study could also be
extended by including more dimensions of attention in scientific research.
This study could be regarded as a start for future monitoring of any
biomedical domain from the perspective of publication types.

Practically speaking, learning how publication type-associated factors
influence knowledge production and diffusion is helpful for policy-makers to
evaluate the scientific significance of related research. The data-driven
approach guarantees a more evidence-based view for policymakers, which in turn
also leads to mutual understanding between scientists and policymakers, and a
more debatable environment [16]. Our result and discussion also provide a
unique perspective for researchers in need of a deeper understanding of not
only the scientific knowledge of one field but also the matters of science
conducting.

##  Appendix A Appendices

###  A.1 Detailed notes on bibliometric data retrieval

We restricted our analysis to publications in the biomedical field.
Publication information including PubMed ID, publication type, published date,
and MeSH [19] terms tagged was extracted from the PubMed database. PubMed data
was downloaded from
https://www.nlm.nih.gov/databases/download/pubmed_medline.html. Results of
Figure 1, 4, 4, Table 2 and S2 used PubMed data downloaded on October 21,
2020. The rest of the results used data downloaded on January 14, 2023. We
excluded publications published later than 2020 due to insufficient recording
and editing by PubMed. Citation relationship between publications used data
downloaded from iCite website
https://nih.figshare.com/collections/iCite_Database_Snapshots_NIH_Open_Citation_Collection_/4586573/32
[40]. Gene information including NCBI ID, symbol, description, and taxonomy ID
was obtained from https://ftp.ncbi.nlm.nih.gov/gene. Data of NCBI gene
supporting Figure 1, 4, 4, Table 2 and S2 was downloaded on December 21. NCBI
gene data supporting the rest of results was downloaded on August 16, 2022.

We used data constructed by PubTator to filter the genes mentioned in titles
and abstracts [88]. The data was downloaded from
https://ftp.ncbi.nlm.nih.gov/pub/lu/PubTatorCentral/. We specifically used
gene2pubtatorcentral.gz. PubTator data supporting Figure 1, 4, Table 2 and S2
was downloaded on October 22, 2020. The rest of the results used PubTator data
downloaded on July 12, 2022.

The data downloaded was parsed by Gustav, which is a framework extracting and
organizing bibliometric data [80]. Gustav converted raw data to Python
dataframes. The gene publication pairs of PubTator were filtered by excluding
the data entries containing ";" in gene columns to avoid ambiguity. We used
the following functions in gustav to construct Python dataframes for our
downstream analyses: ncbi.gene_info, ncbi.gene2pubmed, ncbi.pubmed,
ncbi.pubtator_medline, nih.icite, nlm.mesh.

In order to narrow our focus on scientific publications in the biomedical
sciences, we chose the following publication types for investigations:
“Review”, ”Introductory Journal Article”, ”Comparative Study”, ”Historical
Article”, ”Evaluation Study”, “Validation Study”, “Twin Study”, “Classical
Article”, "Journal Article", "Corrected and Republished Article", "Pragmatic
Clinical Trial", "Observational Study, Veterinary", "Clinical Conference",
"Clinical Study", "Clinical Trial, Veterinary", "Clinical Trial Protocol",
"Clinical Trial, Phase I", "Clinical Trial, Phase II", "Clinical Trial, Phase
III", "Clinical Trial", "Randomized Controlled Trial", "Case Reports",
"Controlled Clinical Trial", "Observational Study", "Meta-Analysis",
"Systematic Review", "Comment", "News", and "Letters".

We observed one issue that the publication types, which contain unusual
clinical study designs, showed a small number of total publication counts on
aging-related genetic research. We, therefore, amalgamated these clinical
publication types as a new category named "Clinical Study" as we defined in
Table 1, under the assumption that the differences in bibliometric
characteristics within clinical publications are trivial. We applied similar
procedures to "Meta-Analysis", "Systematic Review", "Comment", "News", and
"Letters" (Table 1).

We have introduced a new term, "Research Article," which encompasses ordinary
biomedical research publications that may not have undergone certain analyses,
trials, or experiments. PubMed does not classify these publications as
particular biomedical publication types, such as comparative studies or meta-
analyses, which may have distinct research evaluation standards. Note that the
corrected and republished articles not meeting certain classification criteria
were also recognized as "Research Article." Retracted publications were not
covered in the current analysis.

We chose 5 representative species intensively studied both inside and outside
aging-related fields (human, mouse, rat, fly, and C.elegans) for analysis.
After sorting the data, 3863561 publications on human genes, 847282
publications on mouse genes, 550865 publications on rat genes, 57346
publications on fly genes, and 12471 publications on C.elegans genes were
retained.

###  A.2 Collecting the aging-related and nonaging-related citation numbers on
genes

Besides the publication numbers on genes, we assumed that the citation
information of aging-related publications on a gene was also an indicator
describing the research attention paid to such genes. For a publication
focusing on aging processes, the prior knowledge from its references assists
in shaping and conducting the research. The larger volume of references on one
gene suggests a strong influence of this gene on aging-related fields. In this
study, we utilized the citation information from iCite [40] for the analysis
of attention to aging-related fields. The data processing protocols are
summarized below.

  * (1)

In order to count the frequencies that each gene was “cited” by aging-related
research for each publication type and species, we first selected aging-
related publications both belonging to one publication type and studying the
genes of one species. Only genes belonging to the species used for selection
in the first step were considered for the following steps.

  * (2)

We secondly extracted the publications cited by the selected publications in
the first step.

  * (3)

The genes studied by the cited publications in the second step were
identified. We supposed these genes were “cited” by the selected aging-related
publications.

  * (4)

We counted the frequencies of each gene that appeared in the “cited” genes
set. We use these frequencies as the citation numbers on genes for every
publication type and species in aging-related research, named aging-related
citation numbers.

  * (5)

We also calculated the citation numbers in nonaging-related research in a
similar way, but selected nonaging-related publications instead of aging-
related publications in the first step.

###  A.3 Statistically Analyzing the Attentions Across Publication Types by
The Random Effect Model

The descriptive quantities including the aging-related and nonaging-related
publication numbers for each selected publication type for genes were
collected. Here, we assumed that the fraction of aging-related research on
genes specific to publication types and species could reflect the attention to
aging-related studies. The variation of the fractions among publication types
could evaluate their divergence of attention to aging-related studies. The
random effect (RE) model was utilized to quantify the fraction of aging-
related research considering all publication types and to analyze the source
of divergences, more specifically, the research focus on aging processes for a
certain species and the distinctiveness of research pattern of publication
types. Let $F_{\cdot,t}$ be the estimator of aging-related publication
fraction for publication type $t\in T$, say,

$\displaystyle F_{\cdot,t}=\sum\limits_{g\in G}AP_{g,t}/{\sum\limits_{g\in
G}NAP_{g,t}}$ (1)

where $AP_{g,t}$ and $NAP_{g,t}$ are the aging-related and nonaging-related
publication number for gene $g$ and publication type $t$ respectively. $G$
refers to the set containing the total genes with nonzero numbers of
publications. The random effect model can be formulated as:

$\displaystyle F_{\cdot,t}=F+\sigma\cdot\varepsilon_{t}+h_{t}$ (2)

where $F$ is the mean aging-related fraction across pub types,
$\varepsilon_{t}\sim N(0,1)$ describes the fixed effect, $h_{t}\sim
N(0,\tau^{2})$ describes the random effect.

The RE model accounts for the heterogeneity introduced to $F_{\cdot,t}$
depending on the factors associated with publication types by the
heterogeneous variance parameter $\tau^{2}$, and the sampling uncertainty for
the estimates of $F$ by the homogeneous variance parameter $\sigma^{2}$. We
called the R package metafor [85] to estimate $\sigma^{2}$, $\tau^{2}$ and $F$
after inputting the standard deviations and mean values of fractions of aging-
related research for genes of one specific species, i.e.,

$\hat{F}=\frac{\sum\limits_{g}F_{t,g}}{\mbox{total number of genes for
publication type $t$}}.$

Due to the non-normality of the distribution of the fractions for genes, we
adopted bootstrap to obtain more precise estimates of standard deviation by
sampling from the available fractions for genes with the replacement 1000
times.

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## Author Contribution

This article has been developed collaboratively with both authors, Chenxing
Qian and Qingyue Guo. Chenxing Qian: Conceptualization, Data curation, Formal
analysis, Investigation, Methodology, Software, Validation, Visualization,
Writing—original draft, Writing—review & editing. Qingyue Guo:
Conceptualization, Formal analysis, Investigation, Methodology, Validation,
Writing—original draft, Writing—review & editing.

## Declarations

Here we declare no conflicts of interest.

## Supplementart Information

Figure S1: The forest plots displaying the aging-related research publication
fraction for various publication types (with the average aging-related
research publication fraction regarding all publication types) for human (A),
mouse (B), rat (C), C.elegans (D) and fly (E). Figure S2: The forest plots
displaying the aging-related research citation fraction for various
publication types (with the average aging-related research citation fraction
regarding all publication types) for human (A), mouse (B), rat (C), C.elegans
(D) and fly (E).

RE Models Diagnostics Using Publication Fractions
---
Species | $\tau^{2}$ |  $I^{2}$ (percentage) | $H^{2}$ | $Q$ | p-value
Human | 7.4E-05 | 99.529 | 212.270 | 225.222 | 1.7E-04
Mouse | 5.3E-05 | 95.756 | 23.561 | 216.079 | 4.9E-09
Rat | 1.4E-04 | 97.381 | 38.182 | 231.649 | 9.1E-05
C.elegans | 2.4E-04 | 68.097 | 3.134 | 14.198 | 1.4E-12
Fly | 2.5E-05 | 68.672 | 3.192 | 25.175 | 4.0E-12
RE Models Diagnostics Using Citation Fractions
Species | $\tau^{2}$ |  $I^{2}$ (percentage) | $H^{2}$ | $Q$ | p-value
Human | 1.1E-04 | 99.860 | 715.273 | 867.355 | 1.4E-04
Mouse | 2.6E-04 | 99.656 | 290.791 | 740.098 | 1.2E-05
Rat | 1.3E-04 | 98.718 | 78.018 | 339.978 | 3.3E-06
C.elegans | 4.9E-03 | 99.374 | 159.645 | 358.597 | 2.7E-03
Fly | 9.9E-05 | 93.585 | 15.588 | 55.644 | 5.2E-11

Table S1: The key diagnostics of RE model using the publication (and citation)
fractions of publication types for 5 species. Tau square refers to the
estimated amount of heterogeneity introduced by various publication types. H
square and I square are diagnostics considering the degree of heterogeneity
among publication types, among which I square refers to the ratio that total
estimated heterogeneity over total variability and H square refers to the
ratio that total variability over sampling variability. Q is the statistics of
the heterogeneity test, following the student’s distribution with the degree
of freedom equals to the number of publication types. p-value shows the
significance of heterogeneity. Figure S3: The accumulated aging-related
publication numbers of various publication types for 5 species, including
Human (A), Mouse (B), Rat (C), Fly (D), and C. elegans (E) from 1980 to 2020.
The temporal trends of accumulated aging-related publication numbers
considering all selected publication types in this study for the five species
(F).  Figure S4: Venn diagrams displaying the scopes, overlaps, and
differences of genes studied (genes which own at least one publication in a
specific publication type) by various 5 publication types, including Research
Article, Review, Meta-Analysis & Systematic Review (in this figure displayed
as Meta_Analysis), Comparative Study, and Evaluation Study, for Human (A),
Mouse (B), Rat (C), Fly (D) and C.elegans (E).

Publication Number Trends for Human
---
Year | Research | Review | Meta-Analysis, | Comparative | Evaluation | Validation | Twin | Clinical
| Article |  | Systematic Review | Study | Study | Study | Study | Study
2001 | 1129 | 259 | 0 | 146 | 7 | 1 | 4 | 72
2002 | 1172 | 340 | 0 | 154 | 5 | 0 | 2 | 56
2003 | 1183 | 250 | 1 | 321 | 3 | 4 | 2 | 101
2004 | 1198 | 369 | 3 | 265 | 7 | 5 | 1 | 94
2005 | 1301 | 391 | 6 | 314 | 13 | 3 | 6 | 105
2006 | 1416 | 376 | 4 | 310 | 8 | 4 | 6 | 87
2007 | 1560 | 402 | 8 | 185 | 13 | 5 | 10 | 87
2008 | 1765 | 362 | 0 | 215 | 4 | 6 | 4 | 85
2009 | 2299 | 458 | 7 | 230 | 6 | 9 | 6 | 67
2010 | 2559 | 532 | 27 | 265 | 9 | 9 | 5 | 51
2011 | 2889 | 546 | 20 | 336 | 16 | 11 | 4 | 72
2012 | 2917 | 500 | 9 | 290 | 13 | 7 | 33 | 122
2013 | 3186 | 529 | 13 | 343 | 18 | 9 | 5 | 143
2014 | 3335 | 430 | 42 | 185 | 4 | 8 | 2 | 185
2015 | 3465 | 562 | 48 | 182 | 15 | 6 | 5 | 183
2016 | 3720 | 607 | 55 | 161 | 4 | 7 | 13 | 204
2017 | 3587 | 528 | 32 | 148 | 1 | 10 | 0 | 163
2018 | 3395 | 544 | 37 | 140 | 3 | 8 | 2 | 143
Publication Number Trends for Mouse
Year | Research | Review | Meta-Analysis, | Comparative | Evaluation | Validation | Twin | Clinical
| Article |  | Systematic Review | Study | Study | Study | Study | Study
2001 | 690 | 18 | 0 | 85 | 0 | 0 | 0 | 4
2002 | 819 | 48 | 0 | 97 | 0 | 0 | 0 | 0
2003 | 604 | 38 | 0 | 197 | 1 | 3 | 0 | 3
2004 | 629 | 70 | 0 | 202 | 3 | 1 | 0 | 0
2005 | 841 | 89 | 1 | 171 | 1 | 1 | 0 | 0
2006 | 835 | 71 | 0 | 174 | 0 | 0 | 1 | 0
2007 | 1075 | 71 | 0 | 174 | 0 | 0 | 0 | 1
2008 | 1208 | 80 | 0 | 117 | 0 | 0 | 0 | 2
2009 | 1568 | 133 | 0 | 134 | 4 | 3 | 0 | 1
2010 | 1851 | 148 | 0 | 124 | 3 | 0 | 1 | 1
2011 | 1925 | 154 | 1 | 139 | 2 | 1 | 0 | 5
2012 | 1929 | 132 | 1 | 179 | 4 | 0 | 1 | 8
2013 | 2166 | 128 | 4 | 121 | 1 | 6 | 0 | 8
2014 | 2225 | 126 | 3 | 78 | 1 | 0 | 0 | 3
2015 | 2238 | 102 | 3 | 71 | 0 | 3 | 1 | 10
2016 | 2609 | 111 | 0 | 68 | 0 | 1 | 1 | 14
2017 | 2405 | 119 | 1 | 46 | 0 | 0 | 0 | 9
2018 | 2326 | 138 | 11 | 44 | 0 | 0 | 0 | 7
Publication Number Trends for C.elegans
Year | Research | Review | Meta-Analysis, | Comparative | Evaluation | Validation | Twin | Clinical
| Article |  | Systematic Review | Study | Study | Study | Study | Study
2001 | 36 | 1 | 0 | 5 | 0 | 0 | 0 | 0
2002 | 22 | 11 | 0 | 5 | 0 | 0 | 0 | 0
2003 | 37 | 6 | 0 | 0 | 0 | 0 | 0 | 0
2004 | 53 | 6 | 0 | 5 | 0 | 1 | 0 | 0
2005 | 42 | 4 | 0 | 4 | 0 | 0 | 0 | 0
2006 | 75 | 11 | 0 | 6 | 0 | 0 | 0 | 0
2007 | 107 | 6 | 0 | 11 | 0 | 0 | 0 | 0
2008 | 87 | 2 | 0 | 8 | 0 | 0 | 0 | 0
2009 | 142 | 4 | 0 | 1 | 0 | 0 | 0 | 0
2010 | 184 | 3 | 0 | 1 | 3 | 0 | 0 | 0
2011 | 158 | 11 | 0 | 5 | 0 | 0 | 0 | 0
2012 | 133 | 0 | 1 | 0 | 0 | 0 | 0 | 0
2013 | 201 | 9 | 0 | 6 | 0 | 0 | 0 | 0
2014 | 208 | 2 | 0 | 13 | 0 | 0 | 0 | 0
2015 | 192 | 6 | 0 | 0 | 0 | 0 | 0 | 0
2016 | 203 | 2 | 0 | 0 | 0 | 0 | 0 | 0
2017 | 264 | 6 | 0 | 0 | 0 | 0 | 0 | 0
2018 | 218 | 4 | 0 | 16 | 0 | 0 | 0 | 0

Table S2: Annual aging-related publication numbers from 2001 to 2018 for
Human, Mouse and C.elegans

Rank | Research Article | Review
---|---|---
1 | tumor protein p53 | insulin
2 | insulin | tumor protein p53
3 | cyclin-dependent kinase inhibitor 2A | insulin-like growth factor 1
4 | apolipoprotein E | sirtuin 1
5 | interleukin 6 | amyloid beta precursor protein
6 | tumor necrosis factor | tumor necrosis factor
7 | amyloid beta precursor protein | mechanistic target of rapamycin kinase
8 | insulin-like growth factor 1 | interleukin 6
9 | cyclin-dependent kinase inhibitor 1A | apolipoprotein E
10 | CD4 molecule | growth hormone 1
Rank | Comparative Study | Meta-Analysis,Systematic Review
1 | insulin | apolipoprotein E
2 | insulin-like growth factor 1 | insulin
3 | interleukin 6 | interleukin 6
4 | apolipoprotein E | C-reactive protein
5 | tumor protein p53 | insulin like growth factor 1
6 | tumor necrosis factor | parathyroid hormone
7 | growth hormone 1 | interleukin 2
8 | amyloid beta precursor protein | tumor necrosis factor
9 | CD4 molecule | CD4 molecule
10 | CD8a molecule | interferon alpha 1
Rank | Evaluation Study | Validation Study
1 | insulin | insulin
2 | insulin-like growth factor 1 | insulin-like growth factor 1
3 | growth hormone 1 | interleukin 6
4 | tumor protein p53 | C-reactive protein
5 | interleukin 6 | anti-Mullerian hormone
6 | mechanistic target of rapamycin kinase | adiponectin, C1Q and collagen domain containing
7 | apolipoprotein E | apolipoprotein E
8 | renin | amyloid beta precursor protein
9 | ADAM metallopeptidase with thrombospondin type 1 motif 13 | growth hormone 1
10 | von Willebrand factor | microtubule-associated protein tau
Rank | Twin Study | Clinical Study
1 | insulin | insulin
2 | apolipoprotein E | insulin-like growth factor 1
3 | C-reactive protein | growth hormone 1
4 | interleukin 6 | interleukin 6
5 | tumor necrosis factor | C-reactive protein
6 | E1A binding protein p300 | tumor necrosis factor
7 | apolipoprotein A1 | apolipoprotein E
8 | kallikrein-related peptidase 3 | sex hormone binding globulin
9 | clusterin | growth hormone releasing hormone
10 | apolipoprotein B | CD4 molecule

Table S3: The top 10 human genes that were intensively studied in aging-
related research according to the aging-related citation counts on these genes
across 8 major publication types Figure S5: The forest plot forest plots
displaying the aging-related research publication fraction across various
publication types for human without (left) and with (right) selecting 3463
human genes within the overlap of Research Article, Review, Comparative Study,
Meta-Analysis & Systematic Review and Evaluation Study  Figure S6: Enrichment
(log2 of ratio) for aging-related genetic publications with at least 1 human
gene tagged in abstracts or titles over all publications on human genes. Each
circle represents an individual journal; red (blue) circles indicate journals
that significantly enrich (deplete) aging-related genetic publications, with
Bonferroni-corrected p-values using Fisher exact test less than 0.01.

Rank | Review | Bonferroni | Comparative Study | Bonferroni
---|---|---|---|---
1 | Ageing research reviews | 2.29e-42 | Age (Dordrecht, Netherlands) | 3.89e-36
2 | Experimental gerontology | 1.04e-30 | The journals of gerontology. Series A | 1.14e-16
3 | Mechanisms of ageing and development | 7.76e-29 | Experimental gerontology | 1.09e-09
4 | Gerontology | 6.65e-20 | Aging | 2.31e-07
5 | Aging | 2.58e-12 | Biogerontology | 2.27e-06
6 | Rejuvenation research | 6.05e-09 | Neurobiology of aging | 9.51e-04
7 | Current aging science | 1.25e-07 | Aging clinical and experimental research | 2.26e-03
8 | Biogerontology | 4.67e-07 | The journals of gerontology. Series B | 1.03e-02
9 | Aging cell | 4.21e-06 | Australasian J. on ageing | 4.12e-02
10 | The journals of gerontology. Series A | 2.49e-05 | Age and ageing | 4.68e-02
Rank | Meta-Analysis & Systematic Review | Bonferroni | Evaluation Study | Bonferroni
1 | Aging cell | 0.000120 | ACS applied materials & interfaces | 1.0
2 | Rejuvenation research | 0.001198 | Lupus | 1.0
3 | Zeitschrift fur Gerontologie und Geriatrie | 0.030561 | Lung cancer (Amsterdam) | 1.0
4 | The journals of gerontology. Series A | 0.091441 | Lung | 1.0
5 | Ageing research reviews | 0.303205 | Luminescence | 1.0
6 | Medical oncology (Northwood) | 1.0 | Liver transplantation | 1.0
7 | Medical hypotheses | 1.0 | Liver international | 1.0
8 | Medical care | 1.0 | Lipids | 1.0
9 | Mediators of inflammation | 1.0 | Life sciences | 1.0
10 | Medical science monitor | 1.0 | Macromolecular bioscience | 1.0
Rank | Validation Study | Bonferroni | Twin Study | Bonferroni
1 | ACS chemical biology | 1.0 | The journals of gerontology. Series A | 0.47
2 | J. of virological methods | 1.0 | Acta dermato-venereologica | 1.0
3 | J. of virology | 1.0 | Korean J. of ophthalmology : KJO | 1.0
4 | J. of voice | 1.0 | Leukemia | 1.0
5 | Kidney international | 1.0 | Leukemia & lymphoma | 1.0
6 | KSSTA | 1.0 | Metabolic syndrome and related disorders | 1.0
7 | La Medicina del lavoro | 1.0 | Metabolism: clinical and experimental | 1.0
8 | Laboratory investigation | 1.0 | Molecular metabolism | 1.0
9 | Lancet (London) | 1.0 | Molecular psychiatry | 1.0
10 | Learning & memory (NY) | 1.0 | Nature | 1.0
Rank | Clinical Study | Bonferroni | Comment, News, Letter | Bonferroni
1 | Mechanisms of ageing and development | 3.33e-23 | Aging | 1.87e-09
2 | Experimental gerontology | 7.27e-12 | Cell cycle (Georgetown) | 1.13e-05
3 | Age (Dordrecht, Netherlands) | 1.72e-06 | Neurology | 1.28e-02
4 | The journals of gerontology. Series A. | 1.00e-05 | Heart (British Cardiac Society) | 2.27e-02
5 | Aging clinical and experimental research | 5.96e-05 | J. of psychosomatic research | 7.99e-02
6 | J. of bone and mineral research | 3.88e-02 | Rejuvenation research | 2.38e-01
7 | JIFR | 4.27e-02 | Human vaccines & immunotherapeutics | 7.89e-01
8 | BMC geriatrics | 4.27e-02 | Cell stem cell | 9.24e-01
9 | Experimental aging research | 7.71e-02 | Nature medicine | 1
10 | The J. of nutrition, health & aging | 1.27e-01 | Nature metabolism | 1

Table S4: The top 10 journals with the highest proportion of aging-related
publications among 8 different publication types, ranked by Bonferroni
corrected p-values (significance). JIFR: The J. of Injury, Function, and
Rehabilitation; KSSTA: Knee Surgery, Sports Traumatology, Arthroscopyt;