ReviewEndocrinology2021
Population Numbers and Reproductive Health.
Review in Endocrinology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 14 citations in OpenAlex.
- Identification of Dietary Patterns Among Women Undergoing Assisted Reproduction and Its Association with Reproductive Outcomes.International journal of women's health · 2026Article
- Crowdedness imposes stress on tumor metastasis inGenes & diseases · 2025Article
- Maternal cafeteria diet adversely affects the reproductive parameters of rat offspring in a sex-specific manner.Reproductive biology and endocrinology : RB&E · 2025Article
- Article
- Assessment of the Genetic Characteristics of a Generation Born during a Long-Term Socioeconomic Crisis.Genes · 2023Article
- Human Population Density and Reproductive Health: A Changing World Needs Endocrinology.Endocrinology · 2021Article
Corrections and comments
- Commented on by
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A recent study published in The Lancet predicts a remarkable drop in population numbers following a peak that will be reached by 2064. A unique feature of the upcoming population drop is that it will be almost exclusively caused by decreased reproduction, rather than factors that increase rates of mortality. The reasons for decreased reproduction are also unique, as, unlike previous centuries, limited reproduction today is hardly due to a shortage in resources. In other words, the predicted population drop is almost exclusively due to changes in reproductive behavior and reproductive physiology. Today, global changes in reproductive behavior are mostly explained by social sciences in a framework of demographic transition hypotheses, while changes in reproductive physiology are usually attributed to effects of endocrine-disrupting pollutants. This review outlines a complementary/alternative hypothesis, which connects reproductive trends with population densities. Numerous wildlife and experimental studies of a broad range of animal species have demonstrated that reproductive behavior and reproductive physiology are negatively controlled via endocrine and neural signaling in response to increasing population densities. The causal chain of this control system, although not fully understood, includes suppression of every level of hypothalamic-pituitary-gonadal cascade by hypothalamic-pituitary-adrenal axis, activated in response to increasing stress of social interactions. This paper discusses evidence in support of a hypothesis that current trends in reproductive physiology and behavior may be partly explained by increasing population densities. Better understanding of the causal chain involved in reproduction suppression by population density-related factors may help in developing interventions to treat infertility and other reproductive conditions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.