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.

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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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