Tao Huang

Dr. Tao Huang

Department of Epidemiology & Biostatistics, School of Public Health, Peking University

Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

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

Representative Publications

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20 April 2023 in Nature Communications
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Xu GaoXu Gao
Xu Gao
TGTong Geng
Tong Geng
MJMeijie Jiang
Meijie Jiang
NHNinghao Huang
Ninghao Huang
YZYinan Zheng
Yinan Zheng
THTao Huang
Tao Huang
Theory predicts that biological processes of aging may contribute to poor mental health in late life. To test this hypothesis, we evaluated prospective associations between biological age and incident depression and anxiety in 424,299 UK Biobank participants. We measured biological age from clinical traits using the KDM-BA and PhenoAge algorithms. At baseline, participants who were biologically older more often experienced depression/anxiety. During a median of 8.7 years of follow-up, participants with older biological age were at increased risk of incident depression/anxiety (5.9% increase per standard deviation [SD] of KDM-BA acceleration, 95% confidence intervals [CI]: 3.3%–8.5%; 11.3% increase per SD of PhenoAge acceleration, 95% CI: 9.%–13.0%). Biological-aging-associated risk of depression/anxiety was independent of and additive to genetic risk measured by genome-wide-association-study-based polygenic scores. Advanced biological aging may represent a potential risk factor for incident depression/anxiety in midlife and older adults and a potential target for risk assessment and intervention.

4 December 2018 in Journal of the American Heart Association
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MKMarianne Ravn Knop
Marianne Ravn Knop
TGTing‐Ting Geng
Ting‐Ting Geng
AGAlexander Wilhelm Gorny
Alexander Wilhelm Gorny
RDRenyu Ding
Renyu Ding
CLChangwei Li
Changwei Li
THTao Huang
Tao Huang
Background: Low birth weight has been associated with increased risk of type 2 diabetes mellitus, cardiovascular disease, and hypertension, but the risk at high birth weight levels remains uncertain. This systematic review and meta‐analysis aimed to clarify the shape of associations between birth weight and aforementioned diseases in adults and assessed sex‐specific risks. Methods and Results: We systematically searched PubMed, EMBASE , and Web of Science for studies published between 1980 and October 2016. Studies of birth weight and type 2 diabetes mellitus (T2 DM ), cardiovascular disease ( CVD ), and hypertension were included. Random‐effects models were used to derive the summary relative risks and corresponding 95% confidence intervals.We identified 49 studies with 4 053 367 participants assessing the association between birth weight and T2 DM , 33 studies with 5 949 477 participants for CVD , and 53 studies with 4 335 149 participants for hypertension and high blood pressure. Sex‐specific binary analyses showed that only females had an increased risk of T2 DM and CVD at the upper tail of the birth weight distribution. While categorical analyses of 6 birth weight groups and dose‐response analyses showed J‐shaped associations of birth weight with T2 DM and CVD , the association was inverse with hypertension. The lowest risks for T2 DM , CVD , and hypertension were observed at 3.5 to 4.0, 4.0 to 4.5, and 4.0 to 4.5 kg, respectively. Conclusions: These findings indicate that birth weight is associated with risk of T2 DM and CVD in a J‐shaped manner and that this is more pronounced among females.

30 October 2020 in PLOS Medicine
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MGMeng Gao
Meng Gao
JLJun Lv
Jun Lv
CYCanqing Yu
Canqing Yu
YGYu Guo
Yu Guo
ZBZheng Bian
Zheng Bian
FGfor the China Kadoorie Biobank (CKB) Collaborative Group
for the China Kadoorie Biobank (CKB) Collaborative Group
Metabolically healthy obesity (MHO) and its transition to unhealthy metabolic status have been associated with risk of cardiovascular disease (CVD) in Western populations. However, it is unclear to what extent metabolic health changes over time and whether such transition affects risks of subtypes of CVD in Chinese adults. We aimed to examine the association of metabolic health status and its transition with risks of subtypes of vascular disease across body mass index (BMI) categories. The China Kadoorie Biobank was conducted during 25 June 2004 to 15 July 2008 in 5 urban (Harbin, Qingdao, Suzhou, Liuzhou, and Haikou) and 5 rural (Henan, Gansu, Sichuan, Zhejiang, and Hunan) regions across China. BMI and metabolic health information were collected. We classified participants into BMI categories: normal weight (BMI 18.5–23.9 kg/m²), overweight (BMI 24.0–27.9 kg/m²), and obese (BMI ≥ 28 kg/m²). Metabolic health was defined as meeting less than 2 of the following 4 criteria (elevated waist circumference, hypertension, elevated plasma glucose level, and dyslipidemia). The changes in obesity and metabolic health status were defined from baseline to the second resurvey with combination of overweight and obesity. Among the 458,246 participants with complete information and no history of CVD and cancer, the mean age at baseline was 50.9 (SD 10.4) years, and 40.8% were men, and 29.0% were current smokers. During a median 10.0 years of follow-up, 52,251 major vascular events (MVEs), including 7,326 major coronary events (MCEs), 37,992 ischemic heart disease (IHD), and 42,951 strokes were recorded. Compared with metabolically healthy normal weight (MHN), baseline MHO was associated with higher hazard ratios (HRs) for all types of CVD; however, almost 40% of those participants transitioned to metabolically unhealthy status. Stable metabolically unhealthy overweight or obesity (MUOO) (HR 2.22, 95% confidence interval [CI] 2.00–2.47, p < 0.001) and transition from metabolically healthy to unhealthy status (HR 1.53, 1.34–1.75, p < 0.001) were associated with higher risk for MVE, compared with stable healthy normal weight. Similar patterns were observed for MCE, IHD, and stroke. Limitations of the analysis included lack of measurement of lipid components, fasting plasma glucose, and visceral fat, and there might be possible misclassification. Among Chinese adults, MHO individuals have increased risks of MVE. Obesity remains a risk factor for CVD independent of major metabolic factors. Our data further suggest that metabolic health is a transient state for a large proportion of Chinese adults, with the highest vascular risk among those remained MUOO.

2 October 2020 in Journal of Neuroinflammation
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ZZZhenhuang Zhuang
Zhenhuang Zhuang
RYRuotong Yang
Ruotong Yang
WWWenxiu Wang
Wenxiu Wang
Lu QiLu Qi
Lu Qi
THTao Huang
Tao Huang
Background: Growing evidence has shown that alterations in the gut microbiota composition were associated with a variety of neuropsychiatric conditions. However, whether such associations reflect causality remains unknown. We aimed to reveal the causal relationships among gut microbiota, metabolites, and neuropsychiatric disorders including Alzheimer’s disease (AD), major depressive disorder (MDD), and schizophrenia (SCZ). Methods: A two-sample bi-directional Mendelian randomization analysis was performed by using genetic variants from genome-wide association studies as instrumental variables for gut microbiota, metabolites, AD, MDD, and SCZ, respectively. Results: We found suggestive associations of host-genetic-driven increase in Blautia (OR, 0.88; 95%CI, 0.79–0.99; P = 0.028) and elevated γ-aminobutyric acid (GABA) (0.96; 0.92–1.00; P = 0.034), a downstream product of Blautia-dependent arginine metabolism, with a lower risk of AD. Genetically increased Enterobacteriaceae family and Enterobacteriales order were potentially associated with a higher risk of SCZ (1.09; 1.00–1.18; P = 0.048), while Gammaproteobacteria class (0.90; 0.83–0.98; P = 0.011) was related to a lower risk for SCZ. Gut production of serotonin was potentially associated with an increased risk of SCZ (1.07; 1.00–1.15; P = 0.047). Furthermore, genetically increased Bacilli class was related to a higher risk of MDD (1.07; 1.02–1.12; P = 0.010). In the other direction, neuropsychiatric disorders altered gut microbiota composition. Conclusions: These data for the first time provide evidence of potential causal links between gut microbiome and AD, MDD, and SCZ. GABA and serotonin may play an important role in gut microbiota-host crosstalk in AD and SCZ, respectively. Further investigations in understanding the underlying mechanisms of associations between gut microbiota and AD, MDD, and SCZ are required.

14 August 2019 in Diabetes
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JJJinzhu Jia
Jinzhu Jia
PDPan Dou
Pan Dou
MGMeng Gao
Meng Gao
XKXuejun Kong
Xuejun Kong
Changwei LiChangwei Li
Changwei Li
THTao Huang
Tao Huang
We examined the causal direction between gut microbiota–dependent metabolite trimethylamine N-oxide (TMAO) or its predecessors and cardiometabolic diseases, such as risk of type 2 diabetes mellitus (T2DM), coronary artery disease (CAD), myocardial infarction (MI), stroke, atrial fibrillation (AF), and chronic kidney disease (CKD). We used genetic variants as instruments to test the causal associations. Genetically predicted higher TMAO and carnitine were not associated with higher odds of T2DM, AF, CAD, MI, stroke, and CKD after Bonferroni correction (P ≤ 0.0005). However, we observed that genetically increased choline showed a suggestive association with higher risk of T2DM (odds ratio 1.84 [95% CI 1.00–3.42] per 10 units, P = 0.05). In contrast, genetically predicted higher betaine (0.68 [0.48–0.95] per 10 units, P = 0.023) was suggestively associated with a lower risk of T2DM. We observed a suggestive association of genetically increased choline with a lower level of body fat percentage (β ± SE −0.28 ± 0.11, P = 0.013) but a higher estimated glomerular filtration rate (0.10 ± 0.05, P = 0.034). We further found that T2DM (0.130 ± 0.036, P < 0.0001) and CKD (0.483 ± 0.168, P = 0.004) were causally associated with higher TMAO levels. Our Mendelian randomization findings support that T2DM and kidney disease increase TMAO levels and that observational evidence for cardiovascular diseases may be due to confounding or reverse causality.

1 March 2024 in Nature Cardiovascular Research
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MJMeijie Jiang
Meijie Jiang
STSifan Tian
Sifan Tian
SLShuzhen Liu
Shuzhen Liu
YWYuting Wang
Yuting Wang
XGXinbiao Guo
Xinbiao Guo
Xu GaoXu Gao
Xu Gao