Cited by 102Hormonal and reproductive studiesGenetic Associations and EpidemiologySperm and Testicular Function

The DOI matches its Crossref record; 53 of the 56 references it cites were found in a registry or an index; none of the 44 sources its claims cite is checked for support yet.

Causal relationship between obesity and serum testosterone status in men: A bi-directional mendelian randomization analysis

Open access

Abstract

CONTEXT: Obesity in men is associated with low serum testosterone and both are associated with several diseases and increased mortality. OBJECTIVES: Examine the direction and causality of the relationship between body mass index (BMI) and serum testosterone. DESIGN: Bi-directional Mendelian randomization (MR) analysis on prospective cohorts. SETTING: Five cohorts from Denmark, Germany and Sweden (Inter99, SHIP, SHIP Trend, GOOD and MrOS Sweden). PARTICIPANTS: 7446 Caucasian men, genotyped for 97 BMI-associated SNPs and three testosterone-associated SNPs. MAIN OUTCOME MEASURES: BMI and serum testosterone adjusted for age, smoking, time of blood sampling and site. RESULTS: 1 SD genetically instrumented increase in BMI was associated with a 0.25 SD decrease in serum testosterone (IV ratio: -0.25, 95% CI: -0.42--0.09, p = 2.8*10-3). For a body weight reduction altering the BMI from 30 to 25 kg/m2, the effect would equal a 13% increase in serum testosterone. No association was seen for genetically instrumented testosterone with BMI, a finding that was confirmed using large-scale data from the GIANT consortium (n = 104349). CONCLUSIONS: Our results suggest that there is a causal effect of BMI on serum testosterone in men. Population level interventions to reduce BMI are expected to increase serum testosterone in men.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

In a bi-directional Mendelian randomization analysis of 7,446 Caucasian men, genetically instrumented higher BMI causally lowered serum testosterone, while genetically instrumented testosterone did not affect BMI. The authors conclude that reducing BMI is expected to increase serum testosterone in men.

Key findings

  • A 1 SD genetically instrumented increase in BMI was associated with a 0.25 SD decrease in serum testosterone (IV ratio: -0.25, 95% CI: -0.42 to -0.09, p = 2.8×10⁻³); the unweighted score gave -0.29 SD (95% CI: -0.48 to -0.10, p = 2.5×10⁻³).
  • For a BMI reduction from 30 to 25 kg/m², the estimated effect was a 13% increase in serum testosterone using the weighted BMI score and 15% using the unweighted score; the corresponding observational estimate was 16%.
  • No association was seen for genetically instrumented testosterone with BMI; this was confirmed using large-scale data from the GIANT consortium (n = 104,349).
  • The observational association was also significant: 1 SD increase in ln-transformed BMI was associated with a 0.30 SD decrease in serum testosterone (95% CI: -0.35 to -0.24) in random-effects meta-analysis.

Methods

  • Bi-directional Mendelian randomization analysis using five cohorts from Denmark, Germany, and Sweden (Inter99, SHIP, SHIP Trend, GOOD, and MrOS Sweden), including 7,446 Caucasian men with genotype and phenotype data.
  • Genetic instruments were a weighted genetic risk score based on 97 BMI-associated SNPs and a weighted testosterone-decreasing score based on three testosterone-associated SNPs (rs6258, rs12150660, rs5934505); unweighted scores were also used.
  • Main analysis used the IV ratio method with two-stage least squares regression to estimate causal effects of BMI on serum testosterone and vice versa, with adjustment for age, smoking, site, and time of blood sampling where applicable.

Limitations

  • The paper states that participants were unrelated men of European ancestry, limiting generalizability to other ethnic groups.
  • The paper states that the mechanism for the causal effect of BMI on serum testosterone cannot be determined and may involve the hypothalamus-pituitary-gonadal axis.
  • The paper states that genetic markers do not examine effects from extremes of non-linear distributions, so a possible causative effect of severe testosterone deficiency on BMI cannot be discounted.
  • The paper notes a small overlap (<2%) between its cohorts and the BMI GWAS used to weight SNPs, which could bias the weighted score, though similar results with unweighted and FTO-only scores made this unlikely.
  • The paper states that the close connection between testosterone and SHBG makes it difficult to discriminate between a direct effect on serum testosterone and an effect mediated via SHBG.
  • Evident from the text: the MR approach relies on assumptions of no pleiotropy and no confounding, which the authors tested where possible but cannot fully exclude.

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Integrity signals

Registry

Crossref · 10.1371/journal.pone.0176277

Cites

53 of 53 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Fig 1 Observational estimates. An increased BMI is associated with a decreased serum testosterone (T). Linear regression models were used adjusting for age, smoking, site and time of day for blood sample, when applicable. The combined effect was calculated with random effect meta-analysis (I-squared = 82%, p = 0.0005) using all cohorts (n = 7446). Effect sizes are given in standard deviations (SD) of T per SD ln-transformed BMI. Horizontal bars represent 95% confidence intervals.
Fig 1 Observational estimates. An increased BMI is associated with a decreased serum testosterone (T). Linear regression models were used adjusting for age, smoking, site and time of day for blood sample, when applicable. The combined effect was calculated with random effect meta-analysis (I-squared = 82%, p = 0.0005) using all cohorts (n = 7446). Effect sizes are given in standard deviations (SD) of T per SD ln-transformed BMI. Horizontal bars represent 95% confidence intervals.CC BY · PubMed Central
Fig 2 The genetic risk scores’ association with BMI and serum T. A) High weighted genetic risk score on BMI (wGRSBMI) is associated with high BMI. B) The weighted genetic risk score on serum testosterone (T; wGRST) is inversely associated with serum T. C) The weighted genetic risk score on BMI (wGRSBMI) is inversely associated with serum testosterone (T). D) The weighted genetic risk score on serum T (wGRST) is not associated with BMI. Linear regression models were used adjusting for age, smoking, site and time of day for blood sample, when applicable. The combined effect was calculated with fixed-effect meta-analysis (A: I-squared = 0%, p = 0.63; B: I-squared = 0%, p = 0.49; C: I-squared = 0%, p = 0.51; D: I-squared = 0%, p = 0.62) using all cohorts (n = 7446). Effect sizes are given in standard deviations (SD) of ln-transformed BMI per weighted BMI risk allele in A, SD of T per weighted T risk allele in B, SD of T per weighted BMI risk allele in C and SD of ln-transformed BMI per weighted T risk allele in D. Horizontal bars represent 95% confidence intervals.
Fig 2 The genetic risk scores’ association with BMI and serum T. A) High weighted genetic risk score on BMI (wGRSBMI) is associated with high BMI. B) The weighted genetic risk score on serum testosterone (T; wGRST) is inversely associated with serum T. C) The weighted genetic risk score on BMI (wGRSBMI) is inversely associated with serum testosterone (T). D) The weighted genetic risk score on serum T (wGRST) is not associated with BMI. Linear regression models were used adjusting for age, smoking, site and time of day for blood sample, when applicable. The combined effect was calculated with fixed-effect meta-analysis (A: I-squared = 0%, p = 0.63; B: I-squared = 0%, p = 0.49; C: I-squared = 0%, p = 0.51; D: I-squared = 0%, p = 0.62) using all cohorts (n = 7446). Effect sizes are given in standard deviations (SD) of ln-transformed BMI per weighted BMI risk allele in A, SD of T per weighted T risk allele in B, SD of T per weighted BMI risk allele in C and SD of ln-transformed BMI per weighted T risk allele in D. Horizontal bars represent 95% confidence intervals.CC BY · PubMed Central
Fig 3 Genetic and observational estimates of the influence of altering BMI on serum testosterone (T). Effect sizes are given in percent change in serum T based on a decrease in BMI from 30 (cut off for obesity) to 25 (cut off for overweight) kg/m2 (n = 7446). wGRSBMI = weighted genetic risk score on BMI. uwGRSBMI = un-weighted genetic risk score on BMI. Horizontal bars represent 95% confidence intervals.
Fig 3 Genetic and observational estimates of the influence of altering BMI on serum testosterone (T). Effect sizes are given in percent change in serum T based on a decrease in BMI from 30 (cut off for obesity) to 25 (cut off for overweight) kg/m2 (n = 7446). wGRSBMI = weighted genetic risk score on BMI. uwGRSBMI = un-weighted genetic risk score on BMI. Horizontal bars represent 95% confidence intervals.CC BY · PubMed Central

Verification

Claims the paper makes

41 Own result · 23 Not checked yet · 16 Own method · 16 Own interpretation

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  • It may state two separate claims as one. 23
  • Probably not a substantial claim. 10
32 of 32
  • The authors state this as an assumption.

    Genetic variants are randomly distributed with respect to confounding factors because they are fixed at conception.

    “Since genes can be thought of as randomized and fixed at conception, confounding factors will be equally distributed among different genotypes.”
  • The authors state this as an assumption.

    A phenotypic trait cannot cause genetic variation, so Mendelian randomization analyses are free of reverse causation.

    “Furthermore, it will be free of reverse causation since a phenotypic trait cannot cause genetic variation.”
  • The authors state this as an assumption.

    Covariates are randomly distributed with respect to genotype.

    “although covariates are expected to be randomly distributed with respect to genotype, the associations between genotype and known confounders (age and smoking) were examined as this is a key assumption in MR analyses.”
  • The authors state this as an assumption.

    The genotypes are randomized.

    “Three key assumptions underlie the MR randomization approach: the genotypes are randomized; the genetic variants considered as instruments affect the outcome only by modifying the biomarker—that is, these variants have no pleiotropic effects on the outcome; and the genotype is independent of confounders [27, 30, 31].”
  • The authors state this as an assumption.

    The genetic variants considered as instruments affect the outcome only by modifying the biomarker—that is, these variants have no pleiotropic effects on the outcome.

    “Three key assumptions underlie the MR randomization approach: the genotypes are randomized; the genetic variants considered as instruments affect the outcome only by modifying the biomarker—that is, these variants have no pleiotropic effects on the outcome; and the genotype is independent of confounders [27, 30, 31].”
  • The authors state this as an assumption.

    The genotype is independent of confounders.

    “Three key assumptions underlie the MR randomization approach: the genotypes are randomized; the genetic variants considered as instruments affect the outcome only by modifying the biomarker—that is, these variants have no pleiotropic effects on the outcome; and the genotype is independent of confounders [27, 30, 31].”
6 of 6

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Metadata

Authors
Eriksson, J., Haring, R., Grarup, N., Vandenput, L., Wallaschofski, H., Lorentzen, E., Hansen, T., Mellström, D., Pedersen, O., Nauck, M., Lorentzon, M., Nystrup Husemoen, L. L., Völzke, H., Karlsson, M., Baumeister, S. E., Linneberg, A., & Ohlsson, C.
Affiliations
  • University of Gothenburg (SE)
  • Universitätsmedizin Greifswald (DE)
  • University of Copenhagen (DK)
  • Lund University (SE)
Corresponding author
Ohlsson, Claes
Venue
PLOS ONE
Published
2017-04-27
Volume
12
Issue
4
Pages
e0176277
ISSN
1932-6203 (ISSN-L)
OpenAlex
W2609007932
Cited by
101
References
56
Citations by year
2026: 6 · 2025: 6 · 2024: 9 · 2023: 10 · 2022: 15 · 2021: 17 · 2020: 15 · 2019: 10 · 2018: 13
FWCI
8.22
Type
journal article
Language
en
Open access
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
  • Monash University Research Portal (Monash University) (submittedVersion, other-oa)
  • Research Bank (Australian Catholic University) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • University of Regensburg Publication Server (University of Regensburg) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • Lund University Publications (Lund University) (submittedVersion)
Funders
Vetenskapsrådet (2011-2535)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Hormonal and reproductive studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Health Sciences › Medicine › Reproductive Medicine › Sperm and Testicular Function
Keywords
obesity, serum testosterone, body mass index, men, causal relationship, weight loss, androgen deficiency, metabolic syndrome
MeSH terms
Adolescent, Adult, Aged, Humans, Male, Obesity, Phenotype, Testosterone, Body Mass Index, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Young Adult, Mendelian Randomization Analysis

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