Ken Ong

Dr. Ken Ong

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Research Focus & Metrics

Academic Career Length

28Years (1999-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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23 July 2018 in Nature Genetics
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JLJames Lee
James Lee
RWRobbee Wedow
Robbee Wedow
AOAysu Okbay
Aysu Okbay
EKEdward Kong
Edward Kong
OMOmeed Maghzian
Omeed Maghzian
DCDavid Cesarini
David Cesarini

1 August 2006 in Acta Paediatrica
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KOKen Ong
Ken Ong
RLRuth J. F. Loos
Ruth J. F. Loos
In a systematic review, we identified 21 separate studies with data on the association between rapid infancy weight gain, up to age 2 y, and subsequent obesity risk. Uniformly all studies reported significant positive associations. We transformed the reported effect sizes to a standard infancy weight gain exposure, and found that further differences in study design accounted for much of the variation in risk. An accompanying paper by Melinda Yeung reminds us that there are benefits of postnatal catch‐up growth in certain populations, and suggests that genetic and nutritional factors could moderate the unhealthy translation of rapid infancy weight gain to visceral fat and insulin resistance. Further evidence is needed, and we will need to rigorously test the benefits and risks of any interventions. However, the concept of “healthy” rapid catch‐up infancy growth is an attractive prospect.Conclusion: Rapid infancy weight gain is consistently associated with increased subsequent obesity risk, but the predictive ability of different weight gain cut‐offs needs to be tested.

1 January 2012 in Frontiers in Endocrinology
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CECathy E. Elks
Cathy E. Elks
MHMarcel Den Hoed
Marcel Den Hoed
JZJing Hua Zhao
Jing Hua Zhao
SSStephen J. Sharp
Stephen J. Sharp
NWNicholas J. Wareham
Nicholas J. Wareham
KOKen Ong
Ken Ong
Evidence for a major role of genetic factors in the determination of body mass index (BMI) comes from studies of related individuals. Despite consistent evidence for a heritable component of BMI, estimates of BMI heritability vary widely between studies and the reasons for this remain unclear. While some variation is natural due to differences between populations and settings, study design factors may also explain some of the heterogeneity. We performed a systematic review that identified 88 independent estimates of BMI heritability from twin studies (total 140,525 twins) and 27 estimates from family studies (42,968 family members). BMI heritability estimates from twin studies ranged from 0.47 to 0.90 (5th/50th/95th centiles: 0.58/0.75/0.87) and were generally higher than those from family studies (range: 0.24–0.81; 5th/50th/95th centiles: 0.25/0.46/0.68). Meta-regression of the results from twin studies showed that BMI heritability estimates were 0.07 (P = 0.001) higher in children than in adults; estimates increased with mean age among childhood studies (+0.012/year, P = 0.002), but decreased with mean age in adult studies (−0.002/year, P = 0.002). Heritability estimates derived from AE twin models (which assume no contribution of shared environment) were 0.12 higher than those from ACE models (P < 0.001), whilst lower estimates were associated with self reported versus DNA-based determination of zygosity (−0.04, P = 0.02), and with self reported versus measured BMI (−0.05, P = 0.03). Although the observed differences in heritability according to aspects of study design are relatively small, together, the above factors explained 47% of the heterogeneity in estimates of BMI heritability from twin studies. In summary, while some variation in BMI heritability is expected due to population-level differences, study design factors explained nearly half the heterogeneity reported in twin studies. The genetic contribution to BMI appears to vary with age and may have a greater influence during childhood than adult life.

1 May 2019 in Nature Genetics
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ECEGG Consortium
EGG Consortium
NWNicole M. Warrington
Nicole M. Warrington
RBRobin N. Beaumont
Robin N. Beaumont
MHMomoko Horikoshi
Momoko Horikoshi
FDFelix R. Day
Felix R. Day
RFRachel M. Freathy
Rachel M. Freathy

24 April 2017 in Nature Genetics
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TSThe LifeLines Cohort Study
The LifeLines Cohort Study
FDFelix R Day
Felix R Day
TCThe InterAct Consortium
The InterAct Consortium
KIkConFab/AOCS Investigators
kConFab/AOCS Investigators
ECEndometrial Cancer Association Consortium
Endometrial Cancer Association Consortium
JPJohn R B Perry
John R B Perry

19 December 2018 in PLOS Genetics
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FDFelix Day
Felix Day
TKTugce Karaderi
Tugce Karaderi
MJMichelle R. Jones
Michelle R. Jones
CMCindy Meun
Cindy Meun
CHChunyan He
Chunyan He
CWCorrine K. Welt
Corrine K. Welt
Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. Affected women frequently have metabolic disturbances including insulin resistance and dysregulation of glucose homeostasis. PCOS is diagnosed with two different sets of diagnostic criteria, resulting in a phenotypic spectrum of PCOS cases. The genetic similarities between cases diagnosed based on the two criteria have been largely unknown. Previous studies in Chinese and European subjects have identified 16 loci associated with risk of PCOS. We report a fixed-effect, inverse-weighted-variance meta-analysis from 10,074 PCOS cases and 103,164 controls of European ancestry and characterisation of PCOS related traits. We identified 3 novel loci (near PLGRKT, ZBTB16 and MAPRE1), and provide replication of 11 previously reported loci. Only one locus differed significantly in its association by diagnostic criteria; otherwise the genetic architecture was similar between PCOS diagnosed by self-report and PCOS diagnosed by NIH or non-NIH Rotterdam criteria across common variants at 13 loci. Identified variants were associated with hyperandrogenism, gonadotropin regulation and testosterone levels in affected women. Linkage disequilibrium score regression analysis revealed genetic correlations with obesity, fasting insulin, type 2 diabetes, lipid levels and coronary artery disease, indicating shared genetic architecture between metabolic traits and PCOS. Mendelian randomization analyses suggested variants associated with body mass index, fasting insulin, menopause timing, depression and male-pattern balding play a causal role in PCOS. The data thus demonstrate 3 novel loci associated with PCOS and similar genetic architecture for all diagnostic criteria. The data also provide the first genetic evidence for a male phenotype for PCOS and a causal link to depression, a previously hypothesized comorbid disease. Thus, the genetics provide a comprehensive view of PCOS that encompasses multiple diagnostic criteria, gender, reproductive potential and mental health. We performed an international meta-analysis of genome-wide association studies combining over 10,000,000 genetic markers in more than 10,000 European women with polycystic ovary syndrome (PCOS) and 100,000 controls. We found three new risk variants associated with PCOS. Our data demonstrate that the genetic architecture does not differ based on the diagnostic criteria used for PCOS. We also demonstrate a genetic pathway shared with male pattern baldness, representing the first evidence for shared disease biology in men, and shared genetics with depression, previously postulated based only on observational studies.