With improving living standards and socioeconomic development, non-communicable chronic diseases pose a heavy burden on society in both developed and developing countries (Lozano et al., 2012). Obesity is a well-established risk factor for multiple chronic diseases, including cardiovascular disease, diabetes, and cancer (Gallagher and LeRoith, 2015). According to the World Health Organization (WHO), obesity is defined as ‘abnormal or excessive fat accumulation that presents a risk to health’ (World Health Organization, 2000). Body mass index (BMI) is the most appropriate measure for overweight and obesity because the cut-offs account for age, sex, and ethnicity (World Health Organization, 2000). Obesity has increased substantially in many settings, including in Hong Kong. Obesity is complicated with multifactorial risk factors, such as socioeconomic position (SEP), mood disturbance, and genetic factors (Cardel et al., 2020), so there is a need to evaluate their roles in obesity comprehensively and systematically (Manrai et al., 2017). Moreover, given most studies on targets of obesity are conducted in a western setting (Cardel et al., 2020), a comprehensive assessment of modifiable factors of early life obesity in a non-western setting, with a different social structure, provides a valuable opportunity to identify novel exposures.

Environment-wide association studies (EWAS) enable us to assess a variety of exposures across the human environmental exposome in a high-throughput manner (Hall et al., 2013), similar to genome-wide association studies for genetic associations. Previous EWAS have been performed on outcomes, such as type 2 diabetes (Patel et al., 2010), cardiovascular disease (Zhuang et al., 2018), and childhood obesity in western settings (Vrijheid et al., 2020; Uche et al., 2020) but not in a Chinese setting. In the previous EWAS of childhood obesity in the US (6–17 years old), UK, and Europe (6–11 years old), second-hand smoking was related to higher childhood BMI, whilst some other exposures, such as vitamins, were not consistently associated with obesity in these settings (Vrijheid et al., 2020; Uche et al., 2020). Observational studies are open to confounding by SEP, thus assessing associations in a different social context can triangulate the evidence concerning early life obesity.

In addition to the environmental factors, it is increasingly realized that epigenetic factors, which are also modifiable, may play an important role in obesity (Huang et al., 2018a). DNA methylation, which refers to the addition of a methyl group to the 5′ position of a cytosine residue of the DNA, is the most frequently examined epigenetic modification (Fall et al., 2017). DNA methylation may modulate gene expression and thereby influence susceptibility to obesity or obesity-related chronic disease (Fall et al., 2017). Epigenome-wide association study provides an approach to identify the related epigenetic loci in a comprehensive way. For example, DNA methylation at cg06500161 was previously identified as related to obesity in the US (Huang et al., 2018a). Unlike genetic variants, DNA methylation is modifiable and may change in response to environmental factors or disease and therefore might be open to confounding by these factors (Fall et al., 2017; Fraga et al., 2005). As such, findings from western settings may not be generalizable to Chinese populations.

In this situation, Hong Kong, a non-western developed setting, can provide unique insights into health determinants. Most Chinese people in Hong Kong are first-, second-, or third-generation migrants from the neighbouring province of Guangdong in southern China. Dietary habits of people in Hong Kong are similar to those in southern China, although also influenced by western culture (Leung et al., 2003). Lifestyle in Hong Kong also differs from more commonly studied western populations on some important attributes, for example, active smoking among Chinese mothers was rare while maternal exposure to second-hand smoking during pregnancy was common before the smoking ban in public and workplaces was implemented in 2007 (Lee, 2016). Moreover, most current theories concerning the aetiology of and disparities in chronic diseases originate from observations in long-term developed populations of European descent. However, in Hong Kong the economic transition from pre- to post-industrial living conditions has occurred within one lifetime of the older people (Leung et al., 2017), whereas children today in Hong Kong represent the first generation of Chinese to grow up in a post-industrial Chinese setting, which is unrivalled anywhere in the world (Schooling et al., 2012). As such, a study in young Chinese people in Hong Kong, a different setting provides ‘a sentinel for populations currently experiencing very rapid economic development’ (Schooling et al., 2016), which may help identify whether these associations reflect SEP within a specific context or are biologically based as well as having the potential to identify any attributes relevant to the majority of the global population but not necessarily evident in more commonly studied western populations, such as maternal birthplace (Schooling et al., 2010). For example, in the ‘Children of 1997’ birth cohort, a large cohort in Hong Kong, the associations of sugar-sweetened beverages (Zhang et al., 2020), breastfeeding (Hui et al., 2018), milk consumption frequency (Lin et al., 2012), sleep duration (Wang et al., 2019), and parental smoking (Kwok et al., 2010) with childhood and adolescent obesity have been examined, with some important differences detected. The associations for breastfeeding in Hong Kong are much more similar to those seen in randomized controlled trials (RCTs) than those typically seen in western settings (Hui et al., 2018). To take advantage of this unique setting, we conducted an environment- and epigenome-wide association study, to identify further potential drivers of obesity. We focused on puberty because it is an important stage involving a re-orientation from childhood priorities to adulthood (Karlberg, 1989). Exposures associated with obesity at puberty may be important for health in later life (Richardson et al., 2020). Considering that exposures related to obesity at the outset and at the end of puberty may be different, and the associations of the same exposure with obesity may vary by age, we conducted the EWAS at the onset and the end of puberty.

Figures 1 and 2 show the association of each exposure with BMI and WHR at ~11.5 and ~17.6 years. At ~11.5 years, 18 associations with BMI and 19 associations with WHR remained after Bonferroni correction (Figure 1). Of these 18 associations with BMI, 14 associations with BMI remained after controlling for confounders, 13 showed significant association with obesity risk at 11.5 years, and 11 exposures had concordant direction of associations with BMI at ~23 years (Table 2). Of the 19 associations with WHR at ~11.5 years, 7 exposures remained after controlling for confounders, and 6 had the same direction of association with WHR at ~23 years (Table 3). At ~17.6 years, 37 associations with BMI and 19 with WHR remained after Bonferroni correction (Figure 2). Of these 37 associations with BMI, all remained after controlling for confounders, 27 exposures were associated with obesity risk, and 32 exposures had the same direction of association with BMI at ~23 years (Table 4). Of the 19 associations with WHR at ~17.6 years, 12 remained after controlling for confounders, and 7 had the same directions of association with WHR at ~23 years (Table 5).

Figure 1 with 1 supplement see all

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Figure 1—source data 1

The associations for depicting Figure 1a in the study.

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Figure 1—source data 2

The associations for depicting Figure 1b in the study.

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Figure 2

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Figure 2—source data 1

The associations for depicting Figure 2a in the study.

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Figure 2—source data 2

The associations for depicting Figure 2b in the study.

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Specifically, for obesity at ~11.5 years, we found sex (being male), higher birth weight, maternal second-hand smoking, higher parental weight, family history of diabetes, gestational diabetes, and more water consumption were associated with higher BMI, while being small for gestational age and spending more time having meals were associated with lower BMI at ~11.5 years (Table 2). Except for paternal diabetes which was not significant, the rest of exposures all showed consistent associations with obesity risk (Table 2). However, the associations for family history of diabetes and time spent on meals showed inconsistent directions of associations for BMI ~23 years (Table 2). Regarding WHR, the associations were generally consistent with those seen for BMI and showed consistent directions of associations with those at ~17.6 and ~23 years, except for maternal diabetes (Table 3).

For obesity at ~17.6 years, in addition to some shared factors, including sex, birth weight, parental weight, and maternal second-hand smoking, we found some aspects of diet (i.e. more artificially sweetened beverage [ASB], lower-sugar soy milk, reduced-fat/skim milk, Chinese herbal tea, Chinese tea, energy drinks, coffee, and fish consumptions), physical activity, health status (i.e. diabetes, growth problem and snoring), earlier puberty and binge eating associated with higher BMI at ~17.6 years (Table 4). Being a twin, sweets consumption, chocolate consumption, eating before sleep, and having bad dreams were associated with lower BMI at ~17.6 years (Table 4). Regarding the association with obesity risk at 17.6 years, birth weight, being a twin, maternal second-hand smoking, physical activity, and energy drinks intake were not significant but the direction of association was consistent (Table 4). In addition, most exposures had the same direction of association at ~23 years. However, the association of chocolate consumption with BMI at ~23 years was in the other direction. Regarding WHR, the associations were generally consistent with those seen for BMI. Sex, drinking ASB, children’s health status, and coughing or snoring during sleep were also related to WHR at ~17.6 and ~23 years (Table 5). The associations of selected exposures with BMI at ~11.5 and ~17.6 years were similar after adjusting for the time difference between age at anthropometric measurements and age of exposure collection.

Regarding the comparison with RCTs and MR studies, we found RCTs on dark chocolate consumption (Kord-Varkaneh et al., 2019), water consumption promotion (Muckelbauer et al., 2009), and physical activity (Bleich et al., 2018), and MR studies related to drinking coffee (Nordestgaard et al., 2015), dairy intake (Huang et al., 2018b), binge eating (Reed et al., 2017), physical activity (Carrasquilla et al., 2022), snoring (Campos et al., 2020), puberty (Gill et al., 2018; Bell et al., 2018), birth weight (Zanetti et al., 2018), and maternal obesity (Bond et al., 2022; Richmond et al., 2017; Table 6). The available evidence from both RCTs and MR studies show that more physical activity lowers BMI (Bleich et al., 2018), and MR studies also suggest that dairy intake (Yang et al., 2017; Huang et al., 2018b) and binge eating (Reed et al., 2017) are associated with higher BMI.

In the epigenome-wide association study of 286 participants we identified 17 CpGs for BMI at ~23 years in the genes RBM16, SCN2B, SLC24A4, TECPR2, KSR1, RPTOR, GTF3C3, ZNF827, TXNDC15, C2, and RPS6KA2 and 17 for WHR in the genes LANCL2, C6orf195, MIR4535, CTRL, LYRM9, DCDC2, DIRC3, RPS6KA2, LPP, NFIC, MIR7641-2, ZNF141, RNF213, and OPA3 (Tables 7 and 8; Figures 3 and 4). cg14630200 in RPS6KA2 was a shared CpG for both BMI and WHR. The genetic inflation factors (lambda) were 1.006 and 1.005, respectively.

Figure 3

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The associations for depicting Figure 3 in the study.

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Figure 4

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The associations for depicting Figure 4 in the study.

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In this environment- and epigenome-wide association study, we systematically examined associations of over 400 exposures with obesity in a unique Chinese birth cohort, as well as the association of DNA methylation with obesity. Building on the previous studies in this birth cohort (Zhang et al., 2020; Hui et al., 2018; Lin et al., 2012; Kwok et al., 2010), we not only confirmed established risk factors, such as maternal second-hand smoking (Wang et al., 2014), but also added by identifying novel exposures not reported in previous EWAS in western settings (Vrijheid et al., 2020; Uche et al., 2020), such as consumption of ASB and soymilk. The comparison with RCTs or MR studies support a role of higher birth weight, dairy intake, binge eating, and possibly earlier puberty in obesity. We also identified several CpGs related to BMI and WHR in young Chinese, as reported in other populations (Kvaløy et al., 2018).

Our study found that maternal second-hand smoking was consistently associated with obesity at different ages, which is consistent with the concerns repeatedly raised in previous studies (Kwok et al., 2010; Wang et al., 2014), and adds support to the policy of banning smoking cigarettes and alternative smoking products in all indoor areas including workplaces and public places, as well as certain outdoor areas, such as open areas of schools, leisure facilities, bathing beaches, and public transport facilities in Hong Kong (WSC, 2022). Maternal weight is another maternal factor related to higher BMI and WHR consistently at different ages before adulthood. However, recent MR studies do not support a role of maternal overweight in offspring obesity (Bond et al., 2022; Richmond et al., 2017). Gestational diabetes was also identified to be associated with obesity at ~11.5 years, and the positive association remained for obesity at ~17.6 and ~23 years. It would be worthwhile to test its role in MR studies.

Regarding dietary factors, as the dietary assessments were more comprehensively conducted in the Biobank Clinical follow-up, the identified dietary factors were mainly for obesity at ~17.6 years. Interestingly, we found that children who consume more ASB have higher BMI, which is consistent with meta-analyses of cohort studies (Qin et al., 2020; Rousham et al., 2022). Consistent with a previous study in this birth cohort (Zhang et al., 2020), we did not find an association of sugar-sweetened beverages with obesity. The different associations for ASB and sugar-sweetened beverages might be because few consumed sugar-sweetened beverages regularly (6.8% consumed daily) (Zhang et al., 2020), while many consumed ASB (43% participants reported consumption). Consistent with our EWAS, an EWAS in the US also found that consumption of aspartame, a synthetic non-nutritive sweetener, was positively associated with abdominal obesity (Wulaningsih et al., 2017). ASB intake may induce appetite for similar sweet foods, leading to excess energy intake (Mattes and Popkin, 2009). The consistency across settings suggests this association is less likely to be confounded. However, whether it can be used as a target of intervention needs to be tested in a randomized, placebo-controlled trial (Rousham et al., 2022).

Another interesting finding is that milk consumption was not related to obesity at ~11.5 years, while reduced-fat/skim milk consumption was associated with higher BMI at ~17.6 years, with a consistent direction of associations for BMI at ~23 years. Our findings are consistent with a previous study in this cohort, which showed milk consumption frequency was not associated with BMI at 13 years (Lin et al., 2012), however, the previous study did not assess the specific type of milk. Our findings are different from a cross-sectional study in Portugal which shows more skimmed or semi-skimmed milk consumption was associated with lower abdominal obesity (Abreu et al., 2014). An explanation is that the observed associations of reduced-fat or skim milk with higher BMI could be due to increased muscle mass rather than body fat mass, or residual confounding by SEP. Alternatively, it might be due to reverse causality as young people with higher BMI might be more motivated to consume a specific diet. Interestingly, our findings are more consistent with an MR study suggesting genetically predicted higher dairy intake was associated with higher BMI (Huang et al., 2018b).

We also found tea or coffee consumption were associated with higher BMI at ~17.6 years. RCTs of coffee or tea consumption are scarce among children and adolescents because they require long-term adherence. MR studies do not suggest that coffee consumption affects obesity (Nordestgaard et al., 2015; Cornelis and Munafo, 2018), so the observed association might be due to confounding or chance. Similarly, the associations of chocolate and sweets intake with lower BMI at ~17.6 years, as well as physical activity with higher BMI at ~17.6 years are not consistent with RCTs of dark chocolate consumption (Kord-Varkaneh et al., 2019) and physical activity (Bleich et al., 2018), or MR studies of physical activity (Carrasquilla et al., 2022), and might be due to confounding or reverse causality.

Echoing the increasing attention to the role of mood and emotion in obesity control (Cardel et al., 2020), we found that binge eating was associated with higher BMI at ~17.6 years, with a consistent direction of association in the follow-up, consistent with an MR study (Reed et al., 2017). Our findings are also in line with the National Institute for Health and Care Excellence (NICE) guidance which also included binge eating in the consideration of children’s weight management (NICE, 2013). The underlying mechanism has not been clarified, but in general mental wellbeing may be linked to obesity via the neurohormonal weight control network concerning the hypothalamus (Sharma and Kavuru, 2010; Spiegel et al., 2009) as well as via psychosocial factors, lifestyle and behaviour.

As regards lifestyle, consistent with previous observational studies (Wang et al., 2019), we found sleep might play a role in childhood obesity at ~17.6 years. Despite a lack of RCTs, MR findings suggest sleep deprivation may be a causal factor for obesity (Wang et al., 2019; Dashti and Ordovás, 2021). Our study also shows coughing or snoring at night had a positive association with childhood BMI and WHR, which has not been identified in previous EWAS (Vrijheid et al., 2020; Uche et al., 2020). MR studies suggest genetically predicted BMI is positively associated with snoring (Campos et al., 2020), while the association of snoring with BMI is less clear. As such, we cannot exclude the possibility of reverse causality in our observation.

Consistent with previous studies (Lai et al., 2021), we found that earlier pubertal age for girls was related to higher BMI at ~17.6 years. Consistently, a previous study in this birth cohort suggested that maternal age at puberty was associated with offspring BMI at puberty (Lai et al., 2016). However, the findings from MR studies are controversial, with evidence showing genetically predicted earlier age at puberty related to higher BMI (Gill et al., 2018) while another MR study showing puberty timing has a small influence on BMI (Bell et al., 2018). Meanwhile, we cannot exclude a relation in the other direction (Chen et al., 2019); clarifying the bi-directional association would be worthwhile in future studies.

In the epigenome-wide association study, we found DNA methylation at RPS6KA2 was associated with both BMI and WHR, consistent with the previous epigenome-wide association studies of obesity in different populations (Kvaløy et al., 2018). Our study also identified several other genes, such as ZNF827, MIR7641-2, RAPTOR, KSR1, GTF3C3, and NFIC, whose role in obesity or obesity-related disorders has been consistently shown in previous studies. For example, ZNF827, MIR7641-2, and RAPTOR have been reported to be related to obesity (Huang et al., 2015; Dong et al., 2016) and/or overweight (Morris et al., 2015). KSR1 has been reported to be related to the regulation of glucose homeostasis (Klutho et al., 2011), and GTF3C3- to obesity-related dysglycaemia (Andrade et al., 2021). NFIC, which encodes nuclear factor I-C, regulates adipocyte differentiation (Zhou et al., 2017). Opa3, a novel regulator of mitochondrial function, controls thermogenesis and abdominal fat mass (Wells et al., 2012). The consistency of our study with other studies in different settings with different confounding structures suggests these association are less likely to be a product of confounding.

The data used in this study are based on the 'Children of 1997' Birth cohort, maintained by the School of Public Health, The University of Hong Kong. With the approved ethics for this study, the individual participant data cannot be made freely available online. Interested parties can access the data used in this study upon reasonable request, with approval by the birth cohort team. As part of this process, researchers will be required to submit a project proposal for approval, to ensure the data is being used responsibly, ethically, and for scientifically sound projects. Requesters should be employees of a recognized academic institution, health service organization, or charitable research organization with experience in medical research. Requestors should be able to demonstrate, through their peer-reviewed publications in the area of interest, their ability to carry out the proposed study. Source data files have been uploaded for each of the results figures (Figures 1–4) showing the model summary data for plotting the Manhattan plots in environment-wide and epigenome-wide associations with BMI and WHR. Source code for the analyses has been uploaded as Source Code.

1. 1. Abreu S
   2. Santos R
   3. Moreira C
   4. Santos PC
   5. Vale S
   6. Soares-Miranda L
   7. Autran R
   8. Mota J
   9. Moreira P

   (2014) Relationship of milk intake and physical activity to abdominal obesity among adolescents

   Pediatric Obesity 9:71–80.

   https://doi.org/10.1111/j.2047-6310.2012.00130.x

   • PubMed
   • Google Scholar
2. 1. Andrade S
   2. Morais T
   3. Sandovici I
   4. Seabra AL
   5. Constância M
   6. Monteiro MP

   (2021) Adipose tissue epigenetic profile in obesity-related dysglycemia-a systematic review

   Frontiers in Endocrinology 12:681649.

   https://doi.org/10.3389/fendo.2021.681649

   • PubMed
   • Google Scholar
3. 1. Barrera-Gómez J
   2. Agier L
   3. Portengen L
   4. Chadeau-Hyam M
   5. Giorgis-Allemand L
   6. Siroux V
   7. Robinson O
   8. Vlaanderen J
   9. González JR
   10. Nieuwenhuijsen M
   11. Vineis P
   12. Vrijheid M
   13. Vermeulen R
   14. Slama R
   15. Basagaña X

   (2017) A systematic comparison of statistical methods to detect interactions in exposome-health associations

   Environmental Health 16:74.

   https://doi.org/10.1186/s12940-017-0277-6

   • PubMed
   • Google Scholar
4. 1. Bell JA
   2. Carslake D
   3. Wade KH
   4. Richmond RC
   5. Langdon RJ
   6. Vincent EE
   7. Holmes MV
   8. Timpson NJ
   9. Davey Smith G
   10. Rader DJ

   (2018) Influence of puberty timing on adiposity and cardiometabolic traits: a Mendelian randomisation study

   PLOS Medicine 15:e1002641.

   https://doi.org/10.1371/journal.pmed.1002641

   • Google Scholar
5. 1. Bleich SN
   2. Vercammen KA
   3. Zatz LY
   4. Frelier JM
   5. Ebbeling CB
   6. Peeters A

   (2018) Interventions to prevent global childhood overweight and obesity: a systematic review

   The Lancet. Diabetes & Endocrinology 6:332–346.

   https://doi.org/10.1016/S2213-8587(17)30358-3

   • PubMed
   • Google Scholar
6. 1. Bond TA
   2. Richmond RC
   3. Karhunen V
   4. Cuellar-Partida G
   5. Borges MC
   6. Zuber V
   7. Couto Alves A
   8. Mason D
   9. Yang TC
   10. Gunter MJ
   11. Dehghan A
   12. Tzoulaki I
   13. Sebert S
   14. Evans DM
   15. Lewin AM
   16. O’Reilly PF
   17. Lawlor DA
   18. Järvelin M-R

   (2022) Exploring the causal effect of maternal pregnancy adiposity on offspring adiposity: Mendelian randomisation using polygenic risk scores

   BMC Medicine 20:34.

   https://doi.org/10.1186/s12916-021-02216-w

   • PubMed
   • Google Scholar
7. 1. Campos AI
   2. García-Marín LM
   3. Byrne EM
   4. Martin NG
   5. Cuéllar-Partida G
   6. Rentería ME

   (2020) Insights into the aetiology of snoring from observational and genetic investigations in the UK Biobank

   Nature Communications 11:817.

   https://doi.org/10.1038/s41467-020-14625-1

   • PubMed
   • Google Scholar
8. 1. Cardel MI
   2. Atkinson MA
   3. Taveras EM
   4. Holm JC
   5. Kelly AS

   (2020) Obesity treatment among adolescents: a review of current evidence and future directions

   JAMA Pediatrics 174:609–617.

   https://doi.org/10.1001/jamapediatrics.2020.0085

   • PubMed
   • Google Scholar
9. 1. Carrasquilla GD
   2. García-Ureña M
   3. Fall T
   4. Sørensen TIA
   5. Kilpeläinen TO

   (2022) Mendelian randomization suggests a bidirectional, causal relationship between physical inactivity and adiposity

   eLife 11:e70386.

   https://doi.org/10.7554/eLife.70386

   • PubMed
   • Google Scholar
10. 1. Chen YC
    2. Fan HY
    3. Yang C
    4. Hsieh RH
    5. Pan WH
    6. Lee YL

    (2019) Assessing causality between childhood adiposity and early puberty: a bidirectional Mendelian randomization and longitudinal study

    Metabolism 100:153961.

    https://doi.org/10.1016/j.metabol.2019.153961

    • PubMed
    • Google Scholar
11. 1. Cole TJ
    2. Bellizzi MC
    3. Flegal KM
    4. Dietz WH

    (2000) Establishing a standard definition for child overweight and obesity worldwide: international survey

    BMJ 320:1240–1243.

    https://doi.org/10.1136/bmj.320.7244.1240

    • PubMed
    • Google Scholar
12. 1. Cornelis MC
    2. Munafo MR

    (2018) Mendelian randomization studies of coffee and caffeine consumption

    Nutrients 10:1343.

    https://doi.org/10.3390/nu10101343

    • PubMed
    • Google Scholar
13. 1. Curtin F
    2. Schulz P

    (1998) Multiple correlations and bonferroni’s correction

    Biological Psychiatry 44:775–777.

    https://doi.org/10.1016/s0006-3223(98)00043-2

    • PubMed
    • Google Scholar
14. 1. Dashti HS
    2. Ordovás JM

    (2021) Genetics of sleep and insights into its relationship with obesity

    Annual Review of Nutrition 41:223–252.

    https://doi.org/10.1146/annurev-nutr-082018-124258

    • PubMed
    • Google Scholar
15. 1. Dong S-S
    2. Guo Y
    3. Zhu D-L
    4. Chen X-F
    5. Wu X-M
    6. Shen H
    7. Chen X-D
    8. Tan L-J
    9. Tian Q
    10. Deng H-W
    11. Yang T-L

    (2016) Epigenomic elements analyses for promoters identify ESRRG as a new susceptibility gene for obesity-related traits

    International Journal of Obesity 40:1170–1176.

    https://doi.org/10.1038/ijo.2016.44

    • Google Scholar
16. 1. Du P
    2. Zhang X
    3. Huang C-C
    4. Jafari N
    5. Kibbe WA
    6. Hou L
    7. Lin SM

    (2010) Comparison of beta-value and M-value methods for quantifying methylation levels by microarray analysis

    BMC Bioinformatics 11:587.

    https://doi.org/10.1186/1471-2105-11-587

    • Google Scholar
17. 1. Fall T
    2. Mendelson M
    3. Speliotes EK

    (2017) Recent advances in human genetics and epigenetics of adiposity: pathway to precision medicine?

    Gastroenterology 152:1695–1706.

    https://doi.org/10.1053/j.gastro.2017.01.054

    • PubMed
    • Google Scholar
18. 1. Fraga MF
    2. Ballestar E
    3. Paz MF
    4. Ropero S
    5. Setien F
    6. Ballestar ML
    7. Heine-Suñer D
    8. Cigudosa JC
    9. Urioste M
    10. Benitez J
    11. Boix-Chornet M
    12. Sanchez-Aguilera A
    13. Ling C
    14. Carlsson E
    15. Poulsen P
    16. Vaag A
    17. Stephan Z
    18. Spector TD
    19. Wu Y-Z
    20. Plass C
    21. Esteller M

    (2005) Epigenetic differences arise during the lifetime of monozygotic twins

    PNAS 102:10604–10609.

    https://doi.org/10.1073/pnas.0500398102

    • PubMed
    • Google Scholar
19. 1. Gallagher EJ
    2. LeRoith D

    (2015) Obesity and diabetes: the increased risk of cancer and cancer-related mortality

    Physiological Reviews 95:727–748.

    https://doi.org/10.1152/physrev.00030.2014

    • PubMed
    • Google Scholar
20. 1. Geng T
    2. Smith CE
    3. Li C
    4. Huang T

    (2018) Childhood BMI and adult type 2 diabetes, coronary artery diseases, chronic kidney disease, and cardiometabolic traits: a Mendelian randomization analysis

    Diabetes Care 41:1089–1096.

    https://doi.org/10.2337/dc17-2141

    • PubMed
    • Google Scholar
21. 1. Gill D
    2. Brewer CF
    3. Del Greco M F
    4. Sivakumaran P
    5. Bowden J
    6. Sheehan NA
    7. Minelli C

    (2018) Age at menarche and adult body mass index: a Mendelian randomization study

    International Journal of Obesity 42:1574–1581.

    https://doi.org/10.1038/s41366-018-0048-7

    • Google Scholar
22. Conference

    1. Hall MA
    2. Dudek SM
    3. Goodloe R
    4. Crawford DC
    5. Pendergrass SA
    6. Peissig P
    7. Brilliant M
    8. Mccarty CA
    9. Ritchie MD

    (2013) Environment-wide association study (ewas) for type 2 diabetes in the marshfield personalized medicine research project biobank

    Proceedings of the Pacific Symposium. pp. 200–211.

    https://doi.org/10.1142/9789814583220_0020

    • Google Scholar
23. 1. Heiss JA
    2. Just AC

    (2018) Identifying mislabeled and contaminated DNA methylation microarray data: an extended quality control toolset with examples from Geo

    Clinical Epigenetics 10:73.

    https://doi.org/10.1186/s13148-018-0504-1

    • PubMed
    • Google Scholar
24. 1. Huang RC
    2. Garratt ES
    3. Pan H
    4. Wu Y
    5. Davis EA
    6. Barton SJ
    7. Burdge GC
    8. Godfrey KM
    9. Holbrook JD
    10. Lillycrop KA

    (2015) Genome-Wide methylation analysis identifies differentially methylated CpG loci associated with severe obesity in childhood

    Epigenetics 10:995–1005.

    https://doi.org/10.1080/15592294.2015.1080411

    • PubMed
    • Google Scholar
25. 1. Huang JV
    2. Cardenas A
    3. Colicino E
    4. Schooling CM
    5. Rifas-Shiman SL
    6. Agha G
    7. Zheng Y
    8. Hou L
    9. Just AC
    10. Litonjua AA
    11. DeMeo DL
    12. Lin X
    13. Oken E
    14. Hivert M-F
    15. Baccarelli AA

    (2018a) Dna methylation in blood as a mediator of the association of mid-childhood body mass index with cardio-metabolic risk score in early adolescence

    Epigenetics 13:1072–1087.

    https://doi.org/10.1080/15592294.2018.1543503

    • PubMed
    • Google Scholar
26. 1. Huang T
    2. Ding M
    3. Bergholdt Helle KM
    4. Wang T
    5. Heianza Y
    6. Sun D
    7. Frazier-Wood Alexis C
    8. Aslibekyan S
    9. North Kari E
    10. Voortman T
    11. Graff M
    12. Smith Caren E
    13. Lai CQ
    14. Varbo A
    15. Lemaitre RN
    16. de Jonge M
    17. Fumeron F
    18. Corella D
    19. Wang CA
    20. Tjønneland A
    21. Overvad K
    22. Sørensen TIA
    23. Feitosa MF
    24. Wojczynski MK
    25. Kähönen M
    26. Renström F
    27. Psaty BM
    28. Siscovick DS
    29. Barroso I
    30. Johansson I
    31. Hernandez D
    32. Ferrucci L
    33. Bandinelli S
    34. Linneberg A
    35. Zillikens MC
    36. Sandholt CH
    37. Pedersen O
    38. Hansen T
    39. Schulz CA
    40. Sonestedt E
    41. Orho-Melander M
    42. Chen TA
    43. Rotter JI
    44. Allison MA
    45. Rich SS
    46. Sorlí JV
    47. Coltell O
    48. Pennell CE
    49. Eastwood P
    50. Hofman A
    51. Uitterlinden AG
    52. van Rooij FJA
    53. Chu AY
    54. Rose LM
    55. Ridker PM
    56. Viikari J
    57. Raitakari O
    58. Lehtimäki T
    59. Mikkilä V
    60. Willett WC
    61. Wang Y
    62. Tucker KL
    63. Ordovas JM
    64. Kilpeläinen TO
    65. Province MA
    66. Franks PW
    67. Arnett DK
    68. Tanaka T
    69. Toft U
    70. Ericson U
    71. Franco OH
    72. Mozaffarian D
    73. Hu FB
    74. Chasman DI
    75. Nordestgaard BG
    76. Ellervik C
    77. Qi L
    78. Mendelian Randomization of Dairy Consumption Working Group

    (2018b) Dairy consumption and body mass index among adults: Mendelian randomization analysis of 184802 individuals from 25 studies

    Clinical Chemistry 64:183–191.

    https://doi.org/10.1373/clinchem.2017.280701

    • PubMed
    • Google Scholar
27. 1. Hui LL
    2. Li AM
    3. Nelson EAS
    4. Leung GM
    5. Lee SL
    6. Schooling CM

    (2018) In utero exposure to gestational diabetes and adiposity: does breastfeeding make a difference?

    International Journal of Obesity 42:1317–1325.

    https://doi.org/10.1038/s41366-018-0077-2

    • PubMed
    • Google Scholar
28. 1. Karlberg J

    (1989) A biologically-oriented mathematical model (ICP) for human growth

    Acta Paediatrica 78:70–94.

    https://doi.org/10.1111/j.1651-2227.1989.tb11199.x

    • Google Scholar
29. 1. Klutho PJ
    2. Costanzo-Garvey DL
    3. Lewis RE

    (2011) Regulation of glucose homeostasis by KSR1 and MARK2

    PLOS ONE 6:e29304.

    https://doi.org/10.1371/journal.pone.0029304

    • PubMed
    • Google Scholar
30. 1. Kord-Varkaneh H
    2. Ghaedi E
    3. Nazary-Vanani A
    4. Mohammadi H
    5. Shab-Bidar S

    (2019) Does cocoa/dark chocolate supplementation have favorable effect on body weight, body mass index and waist circumference? A systematic review, meta-analysis and dose-response of randomized clinical trials

    Critical Reviews in Food Science and Nutrition 59:2349–2362.

    https://doi.org/10.1080/10408398.2018.1451820

    • PubMed
    • Google Scholar
31. 1. Kvaløy K
    2. Page CM
    3. Holmen TL

    (2018) Epigenome-Wide methylation differences in a group of lean and obese women-a HUNT study

    Scientific Reports 8:16330.

    https://doi.org/10.1038/s41598-018-34003-8

    • PubMed
    • Google Scholar
32. 1. Kwok MK
    2. Schooling CM
    3. Lam TH
    4. Leung GM

    (2010) Paternal smoking and childhood overweight: evidence from the Hong Kong `` children of 1997.''

    Pediatrics 126:e46–e56.

    https://doi.org/10.1542/peds.2009-2642

    • PubMed
    • Google Scholar
33. 1. Lai TC
    2. Au Yeung SL
    3. Lin SL
    4. Leung GM
    5. Schooling CM

    (2016) Brief report: maternal age of menarche and adiposity: evidence from Hong Kong’s `` children of 1997'' birth cohort

    Epidemiology 27:433–437.

    https://doi.org/10.1097/EDE.0000000000000448

    • PubMed
    • Google Scholar
34. 1. Lai X
    2. Fu S
    3. Lin J
    4. Huang S
    5. Yu T
    6. Li X
    7. Pan D
    8. Liu Y
    9. Chen Y
    10. Yu X
    11. Peng J
    12. Zhang B
    13. Feng X
    14. Lin C
    15. Liu S

    (2021) Association of obesity and body fat percentage with pubertal state in six- to nine-year-old Chinese females

    Childhood Obesity 17:525–533.

    https://doi.org/10.1089/chi.2020.0247

    • PubMed
    • Google Scholar
35. 1. Lee PH

    (2014) Is a cutoff of 10 % appropriate for the change-in-estimate criterion of confounder identification?

    Journal of Epidemiology 24:161–167.

    https://doi.org/10.2188/jea.je20130062

    • PubMed
    • Google Scholar
36. 1. Lee JPM

    (2016) Tobacco control policy in Hong Kong

    Hong Kong Medical Journal = Xianggang Yi Xue Za Zhi 22:96–97.

    https://doi.org/10.12809/hkmj164848

    • PubMed
    • Google Scholar
37. 1. Leung GM
    2. Thach TQ
    3. Lam TH
    4. Hedley AJ
    5. Foo W
    6. Fielding R

    (2003) Trends in breast cancer incidence in Hong Kong between 1973 and 1999

    Brit J Cancer 1:6601019.

    https://doi.org/10.1038/sj.bjc.6601019

    • Google Scholar
38. 1. Leung GM
    2. Ni MY
    3. Wong PT
    4. Lee PH
    5. Chan BH
    6. Stewart SM
    7. Schooling CM
    8. Johnston JM
    9. Lam WW
    10. Chan SS
    11. McDowell I
    12. Lam TH
    13. Pang H
    14. Fielding R

    (2017) Cohort profile: family cohort

    International Journal of Epidemiology 46:e1.

    https://doi.org/10.1093/ije/dyu257

    • PubMed
    • Google Scholar
39. 1. Lin SL
    2. Tarrant M
    3. Hui LL
    4. Kwok MK
    5. Lam TH
    6. Leung GM
    7. Schooling CM

    (2012) The role of dairy products and milk in adolescent obesity: evidence from Hong Kong’s `` children of 1997'' birth cohort

    PLOS ONE 7:e52575.

    https://doi.org/10.1371/journal.pone.0052575

    • PubMed
    • Google Scholar
40. 1. Lozano R
    2. Naghavi M
    3. Foreman K
    4. Lim S
    5. Shibuya K
    6. Aboyans V
    7. Abraham J
    8. Adair T
    9. Aggarwal R
    10. Ahn SY
    11. Alvarado M
    12. Anderson HR
    13. Anderson LM
    14. Andrews KG
    15. Atkinson C
    16. Baddour LM
    17. Barker-Collo S
    18. Bartels DH
    19. Bell ML
    20. Benjamin EJ
    21. Bennett D
    22. Bhalla K
    23. Bikbov B
    24. Bin Abdulhak A
    25. Birbeck G
    26. Blyth F
    27. Bolliger I
    28. Boufous S
    29. Bucello C
    30. Burch M
    31. Burney P
    32. Carapetis J
    33. Chen H
    34. Chou D
    35. Chugh SS
    36. Coffeng LE
    37. Colan SD
    38. Colquhoun S
    39. Colson KE
    40. Condon J
    41. Connor MD
    42. Cooper LT
    43. Corriere M
    44. Cortinovis M
    45. de Vaccaro KC
    46. Couser W
    47. Cowie BC
    48. Criqui MH
    49. Cross M
    50. Dabhadkar KC
    51. Dahodwala N
    52. De Leo D
    53. Degenhardt L
    54. Delossantos A
    55. Denenberg J
    56. Des Jarlais DC
    57. Dharmaratne SD
    58. Dorsey ER
    59. Driscoll T
    60. Duber H
    61. Ebel B
    62. Erwin PJ
    63. Espindola P
    64. Ezzati M
    65. Feigin V
    66. Flaxman AD
    67. Forouzanfar MH
    68. Fowkes FGR
    69. Franklin R
    70. Fransen M
    71. Freeman MK
    72. Gabriel SE
    73. Gakidou E
    74. Gaspari F
    75. Gillum RF
    76. Gonzalez-Medina D
    77. Halasa YA
    78. Haring D
    79. Harrison JE
    80. Havmoeller R
    81. Hay RJ
    82. Hoen B
    83. Hotez PJ
    84. Hoy D
    85. Jacobsen KH
    86. James SL
    87. Jasrasaria R
    88. Jayaraman S
    89. Johns N
    90. Karthikeyan G
    91. Kassebaum N
    92. Keren A
    93. Khoo J-P
    94. Knowlton LM
    95. Kobusingye O
    96. Koranteng A
    97. Krishnamurthi R
    98. Lipnick M
    99. Lipshultz SE
    100. Ohno SL
    101. Mabweijano J
    102. MacIntyre MF
    103. Mallinger L
    104. March L
    105. Marks GB
    106. Marks R
    107. Matsumori A
    108. Matzopoulos R
    109. Mayosi BM
    110. McAnulty JH
    111. McDermott MM
    112. McGrath J
    113. Mensah GA
    114. Merriman TR
    115. Michaud C
    116. Miller M
    117. Miller TR
    118. Mock C
    119. Mocumbi AO
    120. Mokdad AA
    121. Moran A
    122. Mulholland K
    123. Nair MN
    124. Naldi L
    125. Narayan KMV
    126. Nasseri K
    127. Norman P
    128. O’Donnell M
    129. Omer SB
    130. Ortblad K
    131. Osborne R
    132. Ozgediz D
    133. Pahari B
    134. Pandian JD
    135. Rivero AP
    136. Padilla RP
    137. Perez-Ruiz F
    138. Perico N
    139. Phillips D
    140. Pierce K
    141. Pope CA
    142. Porrini E
    143. Pourmalek F
    144. Raju M
    145. Ranganathan D
    146. Rehm JT
    147. Rein DB
    148. Remuzzi G
    149. Rivara FP
    150. Roberts T
    151. De León FR
    152. Rosenfeld LC
    153. Rushton L
    154. Sacco RL
    155. Salomon JA
    156. Sampson U
    157. Sanman E
    158. Schwebel DC
    159. Segui-Gomez M
    160. Shepard DS
    161. Singh D
    162. Singleton J
    163. Sliwa K
    164. Smith E
    165. Steer A
    166. Taylor JA
    167. Thomas B
    168. Tleyjeh IM
    169. Towbin JA
    170. Truelsen T
    171. Undurraga EA
    172. Venketasubramanian N
    173. Vijayakumar L
    174. Vos T
    175. Wagner GR
    176. Wang M
    177. Wang W
    178. Watt K
    179. Weinstock MA
    180. Weintraub R
    181. Wilkinson JD
    182. Woolf AD
    183. Wulf S
    184. Yeh P-H
    185. Yip P
    186. Zabetian A
    187. Zheng Z-J
    188. Lopez AD
    189. Murray CJL
    190. AlMazroa MA
    191. Memish ZA

    (2012) Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the global burden of disease study 2010

    Lancet 380:2095–2128.

    https://doi.org/10.1016/S0140-6736(12)61728-0

    • PubMed
    • Google Scholar
41. 1. Manrai AK
    2. Cui Y
    3. Bushel PR
    4. Hall M
    5. Karakitsios S
    6. Mattingly CJ
    7. Ritchie M
    8. Schmitt C
    9. Sarigiannis DA
    10. Thomas DC
    11. Wishart D
    12. Balshaw DM
    13. Patel CJ

    (2017) Informatics and data analytics to support exposome-based discovery for public health

    Annual Review of Public Health 38:279–294.

    https://doi.org/10.1146/annurev-publhealth-082516-012737

    • PubMed
    • Google Scholar
42. 1. Mattes RD
    2. Popkin BM

    (2009) Nonnutritive sweetener consumption in humans: effects on appetite and food intake and their putative mechanisms

    The American Journal of Clinical Nutrition 89:1–14.

    https://doi.org/10.3945/ajcn.2008.26792

    • PubMed
    • Google Scholar
43. 1. Morris BJ
    2. Carnes BA
    3. Chen R
    4. Donlon TA
    5. He Q
    6. Grove JS
    7. Masaki KH
    8. Elliott A
    9. Willcox DC
    10. Allsopp R
    11. Willcox BJ

    (2015) Genetic variation in the raptor gene is associated with overweight but not hypertension in American men of Japanese ancestry

    American Journal of Hypertension 28:508–517.

    https://doi.org/10.1093/ajh/hpu188

    • PubMed
    • Google Scholar
44. 1. Muckelbauer R
    2. Libuda L
    3. Clausen K
    4. Toschke AM
    5. Reinehr T
    6. Kersting M

    (2009) Promotion and provision of drinking water in schools for overweight prevention: randomized, controlled cluster trial

    Pediatrics 123:e661–e667.

    https://doi.org/10.1542/peds.2008-2186

    • PubMed
    • Google Scholar
45. Website

    1. NICE

    (2013) Weight management: lifestyle services for overweight or obese children and young people

    Accessed June 18, 2022.

    https://www.nice.org.uk/guidance/ph47/resources/weight-management-lifestyle-services-for-overweight-or-obese-children-and-young-people-pdf-1996362978757
46. 1. Nordestgaard AT
    2. Thomsen M
    3. Nordestgaard BG

    (2015) Coffee intake and risk of obesity, metabolic syndrome and type 2 diabetes: a Mendelian randomization study

    International Journal of Epidemiology 44:551–565.

    https://doi.org/10.1093/ije/dyv083

    • PubMed
    • Google Scholar
47. 1. Patel CJ
    2. Bhattacharya J
    3. Butte AJ

    (2010) An environment-wide association study (EWAS) on type 2 diabetes mellitus

    PLOS ONE 5:e10746.

    https://doi.org/10.1371/journal.pone.0010746

    • PubMed
    • Google Scholar
48. 1. Qin P
    2. Li Q
    3. Zhao Y
    4. Chen Q
    5. Sun X
    6. Liu Y
    7. Li H
    8. Wang T
    9. Chen X
    10. Zhou Q
    11. Guo C
    12. Zhang D
    13. Tian G
    14. Liu D
    15. Qie R
    16. Han M
    17. Huang S
    18. Wu X
    19. Li Y
    20. Feng Y
    21. Yang X
    22. Hu F
    23. Hu D
    24. Zhang M

    (2020) Sugar and artificially sweetened beverages and risk of obesity, type 2 diabetes mellitus, hypertension, and all-cause mortality: a dose-response meta-analysis of prospective cohort studies

    European Journal of Epidemiology 35:655–671.

    https://doi.org/10.1007/s10654-020-00655-y

    • PubMed
    • Google Scholar
49. 1. Reed ZE
    2. Micali N
    3. Bulik CM
    4. Davey Smith G
    5. Wade KH

    (2017) Assessing the causal role of adiposity on disordered eating in childhood, adolescence, and adulthood: a Mendelian randomization analysis

    The American Journal of Clinical Nutrition 106:764–772.

    https://doi.org/10.3945/ajcn.117.154104

    • PubMed
    • Google Scholar
50. 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
51. 1. Richmond RC
    2. Timpson NJ
    3. Felix JF
    4. Palmer T
    5. Gaillard R
    6. McMahon G
    7. Davey Smith G
    8. Jaddoe VW
    9. Lawlor DA

    (2017) Using genetic variation to explore the causal effect of maternal pregnancy adiposity on future offspring adiposity: a Mendelian randomisation study

    PLOS Medicine 14:e1002221.

    https://doi.org/10.1371/journal.pmed.1002221

    • PubMed
    • Google Scholar
52. Book

    1. Rothman KJ

    (2008)

    Modern Epidemiology

    Wolters Kluwer.

    • Google Scholar
53. 1. Rousham EK
    2. Goudet S
    3. Markey O
    4. Griffiths P
    5. Boxer B
    6. Carroll C
    7. Petherick ES
    8. Pradeilles R

    (2022) Unhealthy food and beverage consumption in children and risk of overweight and obesity: a systematic review and meta-analysis

    Advances in Nutrition 13:1669–1696.

    https://doi.org/10.1093/advances/nmac032

    • PubMed
    • Google Scholar
54. 1. Schooling CM
    2. Yau C
    3. Cowling BJ
    4. Lam TH
    5. Leung GM

    (2010) Socio-Economic disparities of childhood body mass index in a newly developed population: evidence from Hong Kong’s `` children of 1997'' birth cohort

    Archives of Disease in Childhood 95:437–443.

    https://doi.org/10.1136/adc.2009.168542

    • PubMed
    • Google Scholar
55. 1. Schooling CM
    2. Hui LL
    3. Ho LM
    4. Lam TH
    5. Leung GM

    (2012) Cohort profile: `` children of 1997'': a Hong Kong Chinese birth cohort

    International Journal of Epidemiology 41:611–620.

    https://doi.org/10.1093/ije/dyq243

    • PubMed
    • Google Scholar
56. 1. Schooling C
    2. Chan W
    3. Leung S
    4. Lam T
    5. Lee S
    6. Shen C
    7. Leung J
    8. Leung G

    (2016) Cohort profile: Hong Kong department of health elderly health service cohort

    International Journal of Epidemiology 45:64–72.

    https://doi.org/10.1093/ije/dyu227

    • Google Scholar
57. 1. Sharma S
    2. Kavuru M

    (2010) Sleep and metabolism: an overview

    International Journal of Endocrinology 2010:1–12.

    https://doi.org/10.1155/2010/270832

    • Google Scholar
58. 1. Smith GD
    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

    • PubMed
    • Google Scholar
59. 1. Spiegel K
    2. Tasali E
    3. Leproult R
    4. Van Cauter E

    (2009) Effects of poor and short sleep on glucose metabolism and obesity risk

    Nature Reviews Endocrinology 5:253–261.

    https://doi.org/10.1038/nrendo.2009.23

    • Google Scholar
60. 1. Uche UI
    2. Suzuki S
    3. Fulda KG
    4. Zhou Z

    (2020) Environment-wide association study on childhood obesity in the U.S

    Environmental Research 191:110109.

    https://doi.org/10.1016/j.envres.2020.110109

    • PubMed
    • Google Scholar
61. 1. van Iterson M
    2. van Zwet EW
    3. BIOS Consortium
    4. Heijmans BT

    (2017) Controlling bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

    Genome Biology 18:19.

    https://doi.org/10.1186/s13059-016-1131-9

    • PubMed
    • Google Scholar
62. 1. Vrijheid M
    2. Fossati S
    3. Maitre L
    4. Márquez S
    5. Roumeliotaki T
    6. Agier L
    7. Andrusaityte S
    8. Cadiou S
    9. Casas M
    10. de Castro M
    11. Dedele A
    12. Donaire-Gonzalez D
    13. Grazuleviciene R
    14. Haug LS
    15. McEachan R
    16. Meltzer HM
    17. Papadopouplou E
    18. Robinson O
    19. Sakhi AK
    20. Siroux V
    21. Sunyer J
    22. Schwarze PE
    23. Tamayo-Uria I
    24. Urquiza J
    25. Vafeiadi M
    26. Valentin A
    27. Warembourg C
    28. Wright J
    29. Nieuwenhuijsen MJ
    30. Thomsen C
    31. Basagaña X
    32. Slama R
    33. Chatzi L

    (2020) Early-Life environmental exposures and childhood obesity: an exposome-wide approach

    Environmental Health Perspectives 128:67009.

    https://doi.org/10.1289/EHP5975

    • PubMed
    • Google Scholar
63. 1. Wang L
    2. Mamudu HM
    3. Alamian A
    4. Anderson JL
    5. Brooks B

    (2014) Independent and joint effects of prenatal maternal smoking and maternal exposure to second-hand smoke on the development of adolescent obesity: a longitudinal study

    Journal of Paediatrics and Child Health 50:908–915.

    https://doi.org/10.1111/jpc.12667

    • PubMed
    • Google Scholar
64. 1. Wang J
    2. Li AM
    3. Lam HSHS
    4. Leung GM
    5. Schooling CM

    (2019) Sleep duration and adiposity in children and adults: observational and Mendelian randomization studies

    Obesity 27:1013–1022.

    https://doi.org/10.1002/oby.22469

    • Google Scholar
65. 1. Wells T
    2. Davies JR
    3. Guschina IA
    4. Ball DJ
    5. Davies JS
    6. Davies VJ
    7. Evans BAJ
    8. Votruba M

    (2012) Opa3, a novel regulator of mitochondrial function, controls thermogenesis and abdominal fat mass in a mouse model for Costeff syndrome

    Human Molecular Genetics 21:4836–4844.

    https://doi.org/10.1093/hmg/dds315

    • Google Scholar
66. Book

    1. World Health Organization

    (2000)

    Regional Office for the Western P. The Asia-Pacific Perspective: Redefining Obesity and Its Treatment

    Health Communications Australia.

    • Google Scholar
67. Website

    1. WSC

    (2022) The Government of the Hong Kong Special Administrative Region

    Accessed June 18, 2022.

    https://www.info.gov.hk/gia/general/202204/28/P2022042600489.htm
68. 1. Wulaningsih W
    2. Van Hemelrijck M
    3. Tsilidis KK
    4. Tzoulaki I
    5. Patel C
    6. Rohrmann S

    (2017) Investigating nutrition and lifestyle factors as determinants of abdominal obesity: an environment-wide study

    International Journal of Obesity 41:340–347.

    https://doi.org/10.1038/ijo.2016.203

    • PubMed
    • Google Scholar
69. 1. Xu Z
    2. Langie SAS
    3. De Boever P
    4. Taylor JA
    5. Niu L

    (2017) RELIC: a novel dye-bias correction method for illumina methylation beadchip

    BMC Genomics 18:4.

    https://doi.org/10.1186/s12864-016-3426-3

    • PubMed
    • Google Scholar
70. 1. Yang Q
    2. Lin SL
    3. Au Yeung SL
    4. Kwok MK
    5. Xu L
    6. Leung GM
    7. Schooling CM

    (2017) Genetically predicted milk consumption and bone health, ischemic heart disease and type 2 diabetes: A Mendelian randomization study

    European Journal of Clinical Nutrition 71:1008–1012.

    https://doi.org/10.1038/ejcn.2017.8

    • PubMed
    • Google Scholar
71. 1. Zanetti D
    2. Tikkanen E
    3. Gustafsson S
    4. Priest JR
    5. Burgess S
    6. Ingelsson E

    (2018) Birthweight, type 2 diabetes mellitus, and cardiovascular disease

    Circulation 11:002054.

    https://doi.org/10.1161/CIRCGEN.117.002054

    • Google Scholar
72. 1. Zhang T
    2. Au Yeung SL
    3. Kwok MK
    4. Hui LL
    5. Leung GM
    6. Schooling CM

    (2020) Association of sugar-sweetened beverage frequency with adiposity: evidence from the `` children of 1997'' birth cohort

    Nutrients 12:1015.

    https://doi.org/10.3390/nu12041015

    • PubMed
    • Google Scholar
73. 1. Zhou J
    2. Wang S
    3. Qi Q
    4. Yang X
    5. Zhu E
    6. Yuan H
    7. Li X
    8. Liu Y
    9. Li X
    10. Wang B

    (2017) Nuclear factor I-C reciprocally regulates adipocyte and osteoblast differentiation via control of canonical Wnt signaling

    FASEB Journal 31:1939–1952.

    https://doi.org/10.1096/fj.201600975RR

    • PubMed
    • Google Scholar
74. 1. Zhuang X
    2. Guo Y
    3. Ni A
    4. Yang D
    5. Liao L
    6. Zhang S
    7. Zhou H
    8. Sun X
    9. Wang L
    10. Wang X
    11. Liao X

    (2018) Toward a panoramic perspective of the association between environmental factors and cardiovascular disease: an environment-wide association study from national health and nutrition examination survey 1999-2014

    Environment International 118:146–153.

    https://doi.org/10.1016/j.envint.2018.05.046

    • PubMed
    • Google Scholar

Author details

1. Jie Zhao

   School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong

   Contribution

   Conceptualization, Resources, Data curation, Supervision, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Bohan Fan

   For correspondence

   janezhao@hku.hk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1564-0057

2. Bohan Fan

   School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong

   Contribution

   Formal analysis, Visualization, Writing – original draft

   Contributed equally with

   Jie Zhao

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5349-9461

3. Jian Huang

   Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

   Contribution

   Formal analysis, Validation

   Competing interests

   No competing interests declared

4. Benjamin John Cowling

   School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   Benjamin Cowling received research funding from Fosun Pharma, and received honoraria from AstraZeneca, Fosun Pharma, GlaxoSmithKline, Moderna, Pfizer, Roche and Sanofi Pasteur. The author has no other competing interests to declare

   "This ORCID iD identifies the author of this article:" 0000-0002-6297-7154

5. Shiu Lun Ryan Au Yeung

   School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6136-1836

6. Andrea Baccarelli

   Mailman School of Public Health, Columbia University, New York, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Gabriel M Leung

   School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong

   Contribution

   Resources, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

8. C Mary Schooling

   1. School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
   2. City University of New York, School of Public Health and Health policy, New York, United States

   Contribution

   Resources, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9933-5887

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