People living with obesity have elevated levels of the peptide hormone leptin. This can be attributed to the amount of leptin in circulation being proportional to the amount of adipose tissue that an individual has (Obradovic et al., 2021). After being secreted by fat cells in adipose tissue, leptin predominantly acts in the hypothalamus as a major regulator of energy balance (Morrison, 2009). Likewise, both neural and adipose tissues are known to play an important role in the molecular etiology of body mass index (BMI), which is conventionally used to clinically diagnose obesity. However, despite being a cost-effective approach to routinely measure adiposity at scale, BMI is a construct that captures multiple heterogeneous subcomponents. This is epitomized by previous investigations into the functional genes that exert their effects on adiposity via their expression in brain and adipose tissues (Timshel et al., 2020; Rask-Andersen et al., 2019), highlighting the divergent pathways that exist between BMI and downstream complex traits.

Mendelian randomization (MR) is a causal inference technique that can exploit the random segregation of genetic variants within a population to evaluate the genetically predicted effects of modifiable exposures on complex outcomes (Davey Smith and Ebrahim, 2003; Richmond and Davey Smith, 2022). We recently extended the principles of MR to evaluate the separate effects of molecular subcomponents of BMI using genetic variants partitioned by their impact on gene expression derived from subcutaneous adipose tissue (SAT) and brain tissue (known as ‘tissue-partitioned MR’) (Leyden et al., 2022). Using this approach suggested that the brain-derived variants are predominantly responsible for driving the effect of BMI on cardiometabolic disease outcomes (Leyden et al., 2022), which we postulate is due to their involvement in appetite regulation and energy expenditure. These in turn influence the timelines and sites of adipose tissue distribution, which have more adverse consequences at the same level of BMI as do the processes influenced by the SAT-derived variants. Conversely, the SAT-derived set of instruments were predominantly responsible for the effect of BMI on outcomes such as endometrial cancer (Leyden et al., 2023). Differences in components of adiposity at the same level of BMI have previously been demonstrated with these instruments (Leyden et al., 2022), preserving the gene–environment equivalence assumption in MR (Davey Smith, 2012).

In this study, we sought to dissect the causal pathway between BMI and circulating leptin using tissue-partitioned MR by leveraging these brain- and adipose tissue-derived sets of instruments. However, investigating this hypothesis is made even more challenging given that associations between BMI and leptin levels have been reported as early in life as childhood (Shalitin and Phillip, 2003). Therefore, to further develop insight into the relationship between BMI and circulating leptin, we additionally applied another extension that we have developed in recent years known as ‘lifecourse MR’ (Richardson et al., 2020). This approach allows the independent effects of childhood and adult adiposity to be simultaneously estimated on an outcome that can also be measured at separate timepoints in the lifecourse (Richardson et al., 2022b; Sanderson et al., 2022), such as childhood (mean age: 10 y) and adulthood (mean age: 55 y) measures of circulating leptin levels in this study. Taken together, we aimed to conduct a proof-of-concept study for these novel extensions of the conventional MR approach.

In this study, we applied two recent approaches in the MR paradigm, known as lifecourse and tissue partitioned MR, to investigate the influence of adiposity on circulating leptin levels as an exemplar relationship to demonstrate the value of these techniques. Whilst there is irrefutable evidence that having a higher BMI increases circulating levels of leptin, these two extensions of MR methodology provide insight into lifecourse-specific effects and the tissues of action underlying this etiological relationship. We propose that these approaches can be conducted to build upon the findings by conventional MR analyses, which use naturally occurring genetic variants as causal anchors to help establish and estimate the causal effect of modifiable exposures on complex outcomes and disease endpoints.

Our application of lifecourse MR in this work supports a breadth of previous research using this technique, which highlights the importance of taking into consideration the age at which data from participants for exposure and outcome datasets are analyzed. Findings from this study provide evidence that adiposity in childhood exerts a direct effect on leptin levels when measured in early life, corroborating findings from observational studies (Shalitin and Phillip, 2003). Conversely, evidence of an indirect effect along the causal pathway involving adulthood adiposity was found when analyzing leptin measured in later life. This provides a powerful proof of concept for this approach, which has previously found evidence of a direct effect of increased childhood adiposity on lower risk of breast cancer (Richardson et al., 2020), elevated risk of type 1 diabetes (Richardson et al., 2022a), and an influence on cardiac structure (O’Nunain et al., 2022). Evidence supporting indirect effects using this approach, as in this study for adulthood leptin, has been found previously for various cardiovascular (Power et al., 2021) and site-specific cancer endpoints (Mariosa et al., 2022; Papadimitriou et al., 2023).

Additionally, in the multivariable MR analysis of childhood adiposity on childhood leptin, we found that the effect of adulthood adiposity, despite attenuating in comparison to univariable MR estimates, still provided some evidence of an indirect influence. However, within a lifecourse model an effect of adulthood adiposity on childhood leptin levels is impossible given that an adulthood risk factor cannot influence a trait measured at an earlier stage in the lifecourse. We therefore investigated this residual effect of the adulthood adiposity genetic score on in-depth measures of childhood adiposity using data from the age 10 clinic of the ALSPAC study. Similar evidence of a residual effect of the adulthood genetic score on childhood measured DXA-assessed fat mass was found in these analyses, whereas a direct effect of childhood adiposity was found on skinfold measures. These analyses further complement the study design of leveraging both individual- and summary-level data within an MR framework to develop more granular insight into the underlying etiological pathways between risk factors and complex traits. This residual influence of the adulthood BMI score on childhood leptin suggests that the aspects of childhood adiposity that relate to the adulthood genetic score in the multivariable analyses independently of the childhood score importantly influence leptin in childhood. In similar analyses for childhood vitamin D no such relationship was seen (Richardson et al., 2022b). This suggests that different aspects of childhood body composition influence circulating vitamin D and leptin levels, and demonstrates how cognisance of the gene-environment equivalence assumption in MR can motivate improved understanding of genotype to phenotype associations.

Tissue-partitioned MR analyses in this work suggest that the subcomponent of BMI proxied using genetic variants whose effects are putatively mediated via gene expression in brain tissue are predominantly responsible for driving effects of adiposity on leptin during both childhood and adulthood. We postulate that these findings highlight a role for appetite regulation and energy expenditure mechanisms as of fundamental importance in the effect of adiposity on leptin levels. This corroborates findings from the literature, which suggest that being overweight has a downstream consequence on elevated leptin levels due to leptin resistance occurring in the central nervous system, particularly the hypothalamus (Gruzdeva et al., 2019). This is influenced by factors such as blood–brain barrier permeability, which results in leptin failing to suppress appetite and consequently leads to circulating hyperleptinemia amongst patients with obesity (Izquierdo et al., 2019). Taken together with the evidence of an indirect effect highlighted by our lifecourse MR analysis, these findings suggest that leptin may have long-term consequences for appetite suppression. This in turn has an influence on excess body weight, which may start in childhood and then can be sustained into later life. Future research in this space should investigate large-scale genome-wide association studies (GWAS) outcomes related to subcutaneous adipose tissues (e.g. skinfold thickness), particularly given that this is where leptin is primarily produced (Russell et al., 1998).

Fractionating genetic instruments for the same exposure using molecular datasets has important considerations for the (often overlooked) gene–environment equivalence assumption in MR (Davey Smith, 2012). This states that the effect of germline genetic perturbations should have the same downstream consequence on outcomes as if they were caused by the modifiable exposures themselves. For the adipose- and brain tissue-partitioned sets of instruments used in this study, we found previously that the manner in which they relate to downstream disease and complex traits can drastically differ despite having almost identical average effect estimates on BMI as an exposure. In particular, this finding underlines the heterogeneous nature of BMI as a lifestyle risk factor and highlights that this human-derived construct likely captures various causal pathways underlying the relationship between anthropometry and complex outcomes. For example, we previously found evidence that the brain tissue-derived variants are predominantly responsible for the effect of BMI on both cigarette smoking and lung cancer (Leyden et al., 2023). We emphasize that future application of tissue-partitioned MR should carefully consider both the exposure and functionally relevant tissue types being investigated, as well as ensuring that the derived instrument sets are equally predictive of the exposure (Leyden et al., 2023). In particular, applications of a combined approach using both lifecourse and tissue-partitioned MR will likely be most powerful for exposures that have been analyzed by GWAS in large samples at distinct timepoints in the lifecourse (e.g. childhood and adulthood) where the functionally important tissue types have been well characterized by previous studies.

We note that both lifecourse and tissue-partitioned MR have important caveats. For instance, the childhood and adult body size instruments used to disentangle direct and indirect effects in this study do not provide insight into other timepoints over the lifecourse (e.g. adolescence). Future efforts should focus on deriving instruments to separate effects of age-specific adiposity at more granular windows over the lifecourse. Moreover, the childhood adiposity instruments are based on recall data, which is why they required validation in three independent cohorts as described in the ‘Methods’ section. In addition, tissue-partitioned instruments were derived using bulk tissue in this work due to the availability of data and therefore do not take into account cell-type heterogeneity (Glastonbury et al., 2019; Prince et al., 2021). Furthermore, as with all estimates derived from MR, triangulating findings from other orthogonal lines of evidence derived using different study design and datasets provides the most robust conclusions for the approaches applied in this work (Munafò and Davey Smith, 2018).

In summary, our findings highlight a putative role for genes expressed in neural tissues in the etiology of adiposity and leptin levels during both childhood and adulthood. Furthermore, this innovative study provides a proof of concept into how the principles of MR can be adapted to investigate the hypotheses outside the scope of how this causal inference technique was originally conceived (Davey Smith and Ebrahim, 2003).

All individual level data analysed in this study was obtained from the ALSPAC study which is not allowed to be deposited in a public repository. However, all data can be accessed via an application to ALSPAC which requires approval from executive committee (http://www.bristol.ac.uk/alspac/researchers/access/). Summary-level data on adulthood leptin levels were provided by the deCODE Health study which can be found at (https://download.decode.is/form/folder/proteomics). All other data analysed in this study is based on summary-level results as referenced throughout.

1. 1. Agrawal S
   2. Wang M
   3. Klarqvist MDR
   4. Smith K
   5. Shin J
   6. Dashti H
   7. Diamant N
   8. Choi SH
   9. Jurgens SJ
   10. Ellinor PT
   11. Philippakis A
   12. Claussnitzer M
   13. Ng K
   14. Udler MS
   15. Batra P
   16. Khera AV

   (2022) Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots

   Nature Communications 13:3771.

   https://doi.org/10.1038/s41467-022-30931-2

   • PubMed
   • Google Scholar
2. 1. Bowden J
   2. Davey Smith G
   3. Burgess S

   (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

   International Journal of Epidemiology 44:512–525.

   https://doi.org/10.1093/ije/dyv080

   • PubMed
   • Google Scholar
3. 1. Bowden J
   2. Davey Smith G
   3. Haycock PC
   4. Burgess S

   (2016) Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator

   Genetic Epidemiology 40:304–314.

   https://doi.org/10.1002/gepi.21965

   • PubMed
   • Google Scholar
4. 1. Boyd A
   2. Golding J
   3. Macleod J
   4. Lawlor DA
   5. Fraser A
   6. Henderson J
   7. Molloy L
   8. Ness A
   9. Ring S
   10. Davey Smith G

   (2013) Cohort Profile: the ’children of the 90s’--the index offspring of the Avon Longitudinal Study of Parents and Children

   International Journal of Epidemiology 42:111–127.

   https://doi.org/10.1093/ije/dys064

   • PubMed
   • Google Scholar
5. 1. Brandkvist M
   2. Bjørngaard JH
   3. Ødegård RA
   4. Åsvold BO
   5. Davey Smith G
   6. Brumpton B
   7. Hveem K
   8. Richardson TG
   9. Vie GÅ

   (2021) Separating the genetics of childhood and adult obesity: a validation study of genetic scores for body mass index in adolescence and adulthood in the HUNT Study

   Human Molecular Genetics 29:3966–3973.

   https://doi.org/10.1093/hmg/ddaa256

   • Google Scholar
6. 1. Burgess S
   2. Butterworth A
   3. Thompson SG

   (2013) Mendelian randomization analysis with multiple genetic variants using summarized data

   Genetic Epidemiology 37:658–665.

   https://doi.org/10.1002/gepi.21758

   • PubMed
   • Google Scholar
7. 1. Davey Smith G
   2. Ebrahim S

   (2003) Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?*

   International Journal of Epidemiology 32:1–22.

   https://doi.org/10.1093/ije/dyg070

   • Google Scholar
8. 1. Davey Smith G

   (2012) Epigenesis for epidemiologists: does evo-devo have implications for population health research and practice?

   International Journal of Epidemiology 41:236–247.

   https://doi.org/10.1093/ije/dys016

   • PubMed
   • Google Scholar
9. 1. Ferkingstad E
   2. Sulem P
   3. Atlason BA
   4. Sveinbjornsson G
   5. Magnusson MI
   6. Styrmisdottir EL
   7. Gunnarsdottir K
   8. Helgason A
   9. Oddsson A
   10. Halldorsson BV
   11. Jensson BO
   12. Zink F
   13. Halldorsson GH
   14. Masson G
   15. Arnadottir GA
   16. Katrinardottir H
   17. Juliusson K
   18. Magnusson MK
   19. Magnusson OT
   20. Fridriksdottir R
   21. Saevarsdottir S
   22. Gudjonsson SA
   23. Stacey SN
   24. Rognvaldsson S
   25. Eiriksdottir T
   26. Olafsdottir TA
   27. Steinthorsdottir V
   28. Tragante V
   29. Ulfarsson MO
   30. Stefansson H
   31. Jonsdottir I
   32. Holm H
   33. Rafnar T
   34. Melsted P
   35. Saemundsdottir J
   36. Norddahl GL
   37. Lund SH
   38. Gudbjartsson DF
   39. Thorsteinsdottir U
   40. Stefansson K

   (2021) Large-scale integration of the plasma proteome with genetics and disease

   Nature Genetics 53:1712–1721.

   https://doi.org/10.1038/s41588-021-00978-w

   • PubMed
   • Google Scholar
10. 1. Fraser A
    2. Macdonald-Wallis C
    3. Tilling K
    4. Boyd A
    5. Golding J
    6. Davey Smith G
    7. Henderson J
    8. Macleod J
    9. Molloy L
    10. Ness A
    11. Ring S
    12. Nelson SM
    13. Lawlor DA

    (2013) Cohort profile: the avon longitudinal study of parents and children: ALSPAC mothers cohort

    International Journal of Epidemiology 42:97–110.

    https://doi.org/10.1093/ije/dys066

    • PubMed
    • Google Scholar
11. 1. Glastonbury CA
    2. Couto Alves A
    3. El-Sayed Moustafa JS
    4. Small KS

    (2019) Cell-type heterogeneity in adipose tissue is associated with complex traits and reveals disease-relevant cell-specific eQTLs

    American Journal of Human Genetics 104:1013–1024.

    https://doi.org/10.1016/j.ajhg.2019.03.025

    • PubMed
    • Google Scholar
12. 1. Gruzdeva O
    2. Borodkina D
    3. Uchasova E
    4. Dyleva Y
    5. Barbarash O

    (2019) Leptin resistance: underlying mechanisms and diagnosis

    Diabetes, Metabolic Syndrome and Obesity 12:191–198.

    https://doi.org/10.2147/DMSO.S182406

    • PubMed
    • Google Scholar
13. 1. Hemani G
    2. Zheng J
    3. Elsworth B
    4. Wade KH
    5. Haberland V
    6. Baird D
    7. Laurin C
    8. Burgess S
    9. Bowden J
    10. Langdon R
    11. Tan VY
    12. Yarmolinsky J
    13. Shihab HA
    14. Timpson NJ
    15. Evans DM
    16. Relton C
    17. Martin RM
    18. Davey Smith G
    19. Gaunt TR
    20. Haycock PC

    (2018) The MR-Base platform supports systematic causal inference across the human phenome

    eLife 7:e34408.

    https://doi.org/10.7554/eLife.34408

    • PubMed
    • Google Scholar
14. 1. Izquierdo AG
    2. Crujeiras AB
    3. Casanueva FF
    4. Carreira MC

    (2019) Leptin, obesity, and leptin resistance: where are we 25 years later?

    Nutrients 11:2704.

    https://doi.org/10.3390/nu11112704

    • PubMed
    • Google Scholar
15. 1. Leyden GM
    2. Shapland CY
    3. Davey Smith G
    4. Sanderson E
    5. Greenwood MP
    6. Murphy D
    7. Richardson TG

    (2022) Harnessing tissue-specific genetic variation to dissect putative causal pathways between body mass index and cardiometabolic phenotypes

    American Journal of Human Genetics 109:240–252.

    https://doi.org/10.1016/j.ajhg.2021.12.013

    • PubMed
    • Google Scholar
16. 1. Leyden GM
    2. Greenwood MP
    3. Gaborieau V
    4. Han Y
    5. Amos CI
    6. Brennan P
    7. Murphy D
    8. Davey Smith G
    9. Richardson TG

    (2023) Disentangling the aetiological pathways between body mass index and site-specific cancer risk using tissue-partitioned Mendelian randomisation

    British Journal of Cancer 128:618–625.

    https://doi.org/10.1038/s41416-022-02060-6

    • Google Scholar
17. 1. Mariosa D
    2. Smith-Byrne K
    3. Richardson TG
    4. Ferrari P
    5. Gunter MJ
    6. Papadimitriou N
    7. Murphy N
    8. Christakoudi S
    9. Tsilidis KK
    10. Riboli E
    11. Muller D
    12. Purdue MP
    13. Chanock SJ
    14. Hung RJ
    15. Amos CI
    16. O’Mara TA
    17. Amiano P
    18. Pasanisi F
    19. Rodriguez-Barranco M
    20. Krogh V
    21. Tjønneland A
    22. Halkjær J
    23. Perez-Cornago A
    24. Chirlaque M-D
    25. Skeie G
    26. Rylander C
    27. Borch KB
    28. Aune D
    29. Heath AK
    30. Ward HA
    31. Schulze M
    32. Bonet C
    33. Weiderpass E
    34. Davey Smith G
    35. Brennan P
    36. Johansson M

    (2022) Body size at different ages and risk of 6 cancers: a mendelian randomization and prospective cohort study

    Journal of the National Cancer Institute 114:1296–1300.

    https://doi.org/10.1093/jnci/djac061

    • PubMed
    • Google Scholar
18. 1. Morrison CD

    (2009) Leptin signaling in brain: a link between nutrition and cognition?

    Biochimica et Biophysica Acta 1792:401–408.

    https://doi.org/10.1016/j.bbadis.2008.12.004

    • PubMed
    • Google Scholar
19. 1. Munafò MR
    2. Davey Smith G

    (2018) Robust research needs many lines of evidence

    Nature 553:399–401.

    https://doi.org/10.1038/d41586-018-01023-3

    • PubMed
    • Google Scholar
20. 1. Obradovic M
    2. Sudar-Milovanovic E
    3. Soskic S
    4. Essack M
    5. Arya S
    6. Stewart AJ
    7. Gojobori T
    8. Isenovic ER

    (2021) Leptin and obesity: role and clinical implication

    Frontiers in Endocrinology 12:585887.

    https://doi.org/10.3389/fendo.2021.585887

    • PubMed
    • Google Scholar
21. 1. O’Nunain K
    2. Park C
    3. Urquijo H
    4. Leyden GM
    5. Hughes AD
    6. Davey Smith G
    7. Richardson TG

    (2022) A lifecourse mendelian randomization study highlights the long-term influence of childhood body size on later life heart structure

    PLOS Biology 20:e3001656.

    https://doi.org/10.1371/journal.pbio.3001656

    • PubMed
    • Google Scholar
22. 1. Papadimitriou N
    2. Bull CJ
    3. Jenab M
    4. Hughes DJ
    5. Bell JA
    6. Sanderson E
    7. Timpson NJ
    8. Smith GD
    9. Albanes D
    10. Campbell PT
    11. Küry S
    12. Le Marchand L
    13. Ulrich CM
    14. Visvanathan K
    15. Figueiredo JC
    16. Newcomb PA
    17. Pai RK
    18. Peters U
    19. Tsilidis KK
    20. Boer JMA
    21. Vincent EE
    22. Mariosa D
    23. Gunter MJ
    24. Richardson TG
    25. Murphy N

    (2023) Separating the effects of early and later life adiposity on colorectal cancer risk: a Mendelian randomization study

    BMC Medicine 21:5.

    https://doi.org/10.1186/s12916-022-02702-9

    • PubMed
    • Google Scholar
23. 1. Pietzner M
    2. Wheeler E
    3. Carrasco-Zanini J
    4. Cortes A
    5. Koprulu M
    6. Wörheide MA
    7. Oerton E
    8. Cook J
    9. Stewart ID
    10. Kerrison ND
    11. Luan J
    12. Raffler J
    13. Arnold M
    14. Arlt W
    15. O’Rahilly S
    16. Kastenmüller G
    17. Gamazon ER
    18. Hingorani AD
    19. Scott RA
    20. Wareham NJ
    21. Langenberg C

    (2021a) Mapping the proteo-genomic convergence of human diseases

    Science 374:eabj1541.

    https://doi.org/10.1126/science.abj1541

    • PubMed
    • Google Scholar
24. 1. Pietzner M
    2. Wheeler E
    3. Carrasco-Zanini J
    4. Kerrison ND
    5. Oerton E
    6. Koprulu M
    7. Luan J
    8. Hingorani AD
    9. Williams SA
    10. Wareham NJ
    11. Langenberg C

    (2021b) Synergistic insights into human health from aptamer- and antibody-based proteomic profiling

    Nature Communications 12:6822.

    https://doi.org/10.1038/s41467-021-27164-0

    • PubMed
    • Google Scholar
25. 1. Power GM
    2. Tyrrell J
    3. Frayling TM
    4. Davey Smith G
    5. Richardson TG

    (2021) Mendelian randomization analyses suggest childhood body size indirectly influences end points from across the cardiovascular disease spectrum through adult body size

    Journal of the American Heart Association 10:e021503.

    https://doi.org/10.1161/JAHA.121.021503

    • PubMed
    • Google Scholar
26. 1. Prince C
    2. Mitchell RE
    3. Richardson TG

    (2021) Integrative multiomics analysis highlights immune-cell regulatory mechanisms and shared genetic architecture for 14 immune-associated diseases and cancer outcomes

    American Journal of Human Genetics 108:2259–2270.

    https://doi.org/10.1016/j.ajhg.2021.10.003

    • PubMed
    • Google Scholar
27. 1. Rask-Andersen M
    2. Karlsson T
    3. Ek WE
    4. Johansson Å

    (2019) Genome-wide association study of body fat distribution identifies adiposity loci and sex-specific genetic effects

    Nature Communications 10:339.

    https://doi.org/10.1038/s41467-018-08000-4

    • PubMed
    • Google Scholar
28. 1. Richardson TG
    2. Sanderson E
    3. Elsworth B
    4. Tilling K
    5. Davey Smith G

    (2020) Use of genetic variation to separate the effects of early and later life adiposity on disease risk: mendelian randomisation study

    BMJ 369:m1203.

    https://doi.org/10.1136/bmj.m1203

    • PubMed
    • Google Scholar
29. 1. Richardson TG
    2. Mykkänen J
    3. Pahkala K
    4. Ala-Korpela M
    5. Bell JA
    6. Taylor K
    7. Viikari J
    8. Lehtimäki T
    9. Raitakari O
    10. Davey Smith G

    (2021) Evaluating the direct effects of childhood adiposity on adult systemic metabolism: a multivariable Mendelian randomization analysis

    International Journal of Epidemiology 50:1580–1592.

    https://doi.org/10.1093/ije/dyab051

    • PubMed
    • Google Scholar
30. 1. Richardson TG
    2. Crouch DJM
    3. Power GM
    4. Morales-Berstein F
    5. Hazelwood E
    6. Fang S
    7. Cho Y
    8. Inshaw JRJ
    9. Robertson CC
    10. Sidore C
    11. Cucca F
    12. Rich SS
    13. Todd JA
    14. Davey Smith G

    (2022a) Childhood body size directly increases type 1 diabetes risk based on a lifecourse Mendelian randomization approach

    Nature Communications 13:2337.

    https://doi.org/10.1038/s41467-022-29932-y

    • PubMed
    • Google Scholar
31. 1. Richardson TG
    2. Power GM
    3. Davey Smith G

    (2022b) Adiposity may confound the association between vitamin D and disease risk - a lifecourse Mendelian randomization study

    eLife 11:e79798.

    https://doi.org/10.7554/eLife.79798

    • PubMed
    • Google Scholar
32. 1. Richmond RC
    2. Davey Smith G

    (2022) Mendelian randomization: concepts and scope

    Cold Spring Harbor Perspectives in Medicine 12:a040501.

    https://doi.org/10.1101/cshperspect.a040501

    • PubMed
    • Google Scholar
33. 1. Russell CD
    2. Petersen RN
    3. Rao SP
    4. Ricci MR
    5. Prasad A
    6. Zhang Y
    7. Brolin RE
    8. Fried SK

    (1998) Leptin expression in adipose tissue from obese humans: depot-specific regulation by insulin and dexamethasone

    The American Journal of Physiology 275:E507–E515.

    https://doi.org/10.1152/ajpendo.1998.275.3.E507

    • PubMed
    • Google Scholar
34. 1. Sanderson E
    2. Davey Smith G
    3. Windmeijer F
    4. Bowden J

    (2019) An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings

    International Journal of Epidemiology 48:713–727.

    https://doi.org/10.1093/ije/dyy262

    • PubMed
    • Google Scholar
35. 1. Sanderson E
    2. Richardson TG
    3. Morris TT
    4. Tilling K
    5. Davey Smith G

    (2022) Estimation of causal effects of a time-varying exposure at multiple time points through multivariable mendelian randomization

    PLOS Genetics 18:e1010290.

    https://doi.org/10.1371/journal.pgen.1010290

    • PubMed
    • Google Scholar
36. 1. Shalitin S
    2. Phillip M

    (2003) Role of obesity and leptin in the pubertal process and pubertal growth--a review

    International Journal of Obesity and Related Metabolic Disorders 27:869–874.

    https://doi.org/10.1038/sj.ijo.0802328

    • PubMed
    • Google Scholar
37. 1. Timshel PN
    2. Thompson JJ
    3. Pers TH

    (2020) Genetic mapping of etiologic brain cell types for obesity

    eLife 9:e55851.

    https://doi.org/10.7554/eLife.55851

    • PubMed
    • Google Scholar

Author details

1. Tom G Richardson

   MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom

   Contribution

   Data curation, Formal analysis, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   Tom.G.Richardson@bristol.ac.uk

   Competing interests

   TGR is an employee of GlaxoSmithKline outside of this work

   "This ORCID iD identifies the author of this article:" 0000-0002-7918-2040

2. Genevieve M Leyden

   MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom

   Contribution

   Data curation, Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. George Davey Smith

   MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1407-8314