| 1. |
- Ahmad, Shafqat, et al.
(författare)
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Effect of General Adiposity and Central Body Fat Distribution on the Circulating Metabolome : A Multi-Cohort Nontargeted Metabolomics Observational and Mendelian Randomization Study
- 2022
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Ingår i: Diabetes. - : American Diabetes Association. - 0012-1797 .- 1939-327X. ; 71:2, s. 329-339
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Tidskriftsartikel (refereegranskat)abstract
- Obesity is associated with adverse health outcomes, but the metabolic effects have not yet been fully elucidated. We aimed to investigate the association between adiposity with circulating metabolites and to address causality with Mendelian randomization (MR). Metabolomics data was generated by non-targeted ultra-performance liquid-chromatography coupled to time-of-flight mass-spectrometry in plasma and serum from three population-based Swedish cohorts: ULSAM (N=1,135), PIVUS (N=970), and TwinGene (N=2,059). We assessed associations between general adiposity measured as body mass index (BMI) and central body fat distribution measured as waist-to-hip ratio adjusted for BMI (WHRadjBMI) with 210 annotated metabolites. We employed MR analysis to assess causal effects. Lastly, we attempted to replicate the MR findings in the KORA and TwinsUK cohorts (N=7,373), the CHARGE consortium (N=8,631), the Framingham Heart Study (N=2,076) and the DIRECT consortium (N=3,029). BMI was associated with 77 metabolites, while WHRadjBMI was associated with 11 and 3 metabolites in women and men, respectively. The MR analyses in the Swedish cohorts suggested a causal association (p-value <0.05) of increased general adiposity and reduced levels of arachidonic acid, dodecanedioic acid and lysophosphatidylcholine (P-16:0) as well as with increased creatine levels. The replication effort provided support for a causal association of adiposity on reduced levels of arachidonic acid (p-value 0.03). Adiposity is associated with variation of large parts of the circulating metabolome, however causality needs further investigation in well-powered cohorts.
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| 2. |
- Ganna, Andrea, 1985-, et al.
(författare)
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Genetic determinants of mortality : Can findings from genome-wide association studies explain variation in human mortality?
- 2013
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Ingår i: Human Genetics. - : Springer Science and Business Media LLC. - 0340-6717 .- 1432-1203. ; 132:5, s. 553-561
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Tidskriftsartikel (refereegranskat)abstract
- Twin studies have estimated the heritability of longevity to be approximately 20-30 %. Genome-wide association studies (GWAS) have revealed a large number of determinants of morbidity, but so far, no new polymorphisms have been discovered to be associated with longevity per se in GWAS. We aim to determine whether the genetic architecture of mortality can be explained by single nucleotide polymorphisms (SNPs) associated with common traits and diseases related to mortality. By extensive quality control of published GWAS we created a genetic score from 707 common SNPs associated with 125 diseases or risk factors related with overall mortality. We prospectively studied the association of the genetic score with: (1) time-to-death; (2) incidence of the first of nine major diseases (coronary heart disease, stroke, heart failure, diabetes, dementia, lung, breast, colon and prostate cancers) in two population-based cohorts of Dutch and Swedish individuals (N = 15,039; age range 47-99 years). During a median follow-up of 6.3 years (max 22.2 years), we observed 4,318 deaths and 2,132 incident disease events. The genetic score was significantly associated with time-to-death [hazard ratio (HR) per added risk allele = 1.003, P value = 0.006; HR 4th vs. 1st quartile = 1.103]. The association between the genetic score and incidence of major diseases was stronger (HR per added risk allele = 1.004, P value = 0.002; HR 4th vs. 1st quartile = 1.160). Associations were stronger for individuals dying at older ages. Our findings are compatible with the view of mortality as a complex and highly polygenetic trait, not easily explainable by common genetic variants related to diseases and physiological traits.
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| 3. |
- Ganna, Andrea, 1985-, et al.
(författare)
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Risk prediction measures for case-cohort and nested case-control designs : an application to cardiovascular disease.
- 2012
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Ingår i: American Journal of Epidemiology. - : Oxford University Press (OUP). - 0002-9262 .- 1476-6256. ; 175:7, s. 715-724
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Tidskriftsartikel (refereegranskat)abstract
- Case-cohort and nested case-control designs are often used to select an appropriate subsample of individuals from prospective cohort studies. Despite the great attention that has been given to the calculation of association estimators, no formal methods have been described for estimating risk prediction measures from these 2 sampling designs. Using real data from the Swedish Twin Registry (2004-2009), the authors sampled unstratified and stratified (matched) case-cohort and nested case-control subsamples and compared them with the full cohort (as "gold standard"). The real biomarker (high density lipoprotein cholesterol) and simulated biomarkers (BIO1 and BIO2) were studied in terms of association with cardiovascular disease, individual risk of cardiovascular disease at 3 years, and main prediction metrics. Overall, stratification improved efficiency, with stratified case-cohort designs being comparable to matched nested case-control designs. Individual risks and prediction measures calculated by using case-cohort and nested case-control designs after appropriate reweighting could be assessed with good efficiency, except for the finely matched nested case-control design, where matching variables could not be included in the individual risk estimation. In conclusion, the authors have shown that case-cohort and nested case-control designs can be used in settings where the research aim is to evaluate the prediction ability of new markers and that matching strategies for nested case-control designs may lead to biased prediction measures.
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| 4. |
- Salihovic, Samira, 1985-, et al.
(författare)
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Identification of metabolic profiles associated with human exposure to perfluoroalkyl substances
- 2019
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Ingår i: Journal of Exposure Science and Environmental Epidemiology. - : Nature Publishing Group. - 1559-0631 .- 1559-064X. ; 29:2, s. 196-205
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Tidskriftsartikel (refereegranskat)abstract
- Recent epidemiological studies suggest that human exposure to perfluoroalkyl substances (PFASs) may be associated with type 2 diabetes and other metabolic phenotypes. To gain further insights regarding PFASs exposure in humans, we here aimed to characterize the associations between different PFASs and the metabolome. In this cross-sectional study, we investigated 965 individuals from Sweden (all aged 70 years, 50% women) sampled in 2001-2004. PFASs were analyzed in plasma using isotope-dilution ultra-pressure liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS). Non-target metabolomics profiling was performed in plasma using UPLC coupled to time-of-flight mass spectrometry (UPLC-QTOFMS) operated in positive electrospray mode. Multivariate linear regression analysis was used to investigate associations between circulating levels of PFASs and metabolites. In total, 15 metabolites, predominantly from lipid pathways, were associated with levels of PFASs following adjustment for sex, smoking, exercise habits, education, energy, and alcohol intake, after correction for multiple testing. Perfluorononanoic acid (PFNA) and perfluoroundecanoic acid (PFUnDA) were strongly associated with multiple glycerophosphocholines and fatty acids including docosapentaenoic acid (DPA) and docosahexaenoic acid (DHA). We also found that the different PFASs evaluated were associated with distinctive metabolic profiles, suggesting potentially different biochemical pathways in humans.
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| 5. |
- Salihovic, Samira, Associate Senior Lecturer, 1985-, et al.
(författare)
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Non-targeted urine metabolomics and associations with prevalent and incident type 2 diabetes
- 2020
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Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Better risk prediction and new molecular targets are key priorities in type 2 diabetes (T2D) research. Little is known about the role of the urine metabolome in predicting the risk of T2D. We aimed to use non-targeted urine metabolomics to discover biomarkers and improve risk prediction for T2D. Urine samples from two community cohorts of 1,424 adults were analyzed by ultra-performance liquid chromatography/mass spectrometry (UPLC-MS). In a discovery/replication design, three out of 62 annotated metabolites were associated with prevalent T2D, notably lower urine levels of 3-hydroxyundecanoyl-carnitine. In participants without diabetes at baseline, LASSO regression in the training set selected six metabolites that improved prediction of T2D beyond established risk factors risk over up to 12 years' follow-up in the test sample, from C-statistic 0.866 to 0.892. Our results in one of the largest non-targeted urinary metabolomics study to date demonstrate the role of the urine metabolome in identifying at-risk persons for T2D and suggest urine 3-hydroxyundecanoyl-carnitine as a biomarker candidate.
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| 6. |
- Salihovic, Samira, 1985-, et al.
(författare)
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The metabolic fingerprint of p,p'-DDE and HCB exposure in humans
- 2016
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Ingår i: Environment International. - Oxford, United Kingdom : Elsevier. - 0160-4120 .- 1873-6750. ; 88, s. 60-66
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Tidskriftsartikel (refereegranskat)abstract
- Background: Dichlorodiphenyldichloroethylene (p,p'-DDE) and hexachlorobenzene (HCB) are organochlorine pesticides with well-known endocrine disrupting properties. Exposure to p,p'-DDE and HCB concerns human populations worldwide and has been linked to metabolic disorders such as obesity and type 2 diabetes, but details about these associations in humans from the general population are largely unknown.Objectives: We investigated the associations between p,p'-DDE and HCB exposure and global metabolomic profiles in serum samples from 1016 participants from the Swedish population-based Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study.Methods: HCB and p,p'-DDE levels were determined using gas chromatography coupled to high-resolution mass spectrometry (GC-HRMS). Metabolite levels were determined by using a non-targeted metabolomics approach with ultra-performance liquid chromatography coupled to time-of- flight mass spectrometry (UPLC-TOFMS). Association analyses were performed using multivariate linear regression.Results: We found circulating levels of p,p-DDE and HCB to be significantly associated with circulating levels of 16 metabolites following adjustment for age, sex, education level, exercise habits, smoking, energy intake, and alcohol intake. The majority of the 16 metabolites belong to lipid metabolism pathways and include fatty acids, glycerophospholipids, sphingolipids, and glycerolipids. Overall, p,p'-DDE and HCB levels were found to be correlated to different metabolites, which suggests that different metabolic fingerprints may be related to circulating levels of these two pesticides.Conclusions: Our findings establish a link between human exposure to organochlorine pesticides and metabolites of key metabolic processes mainly related to human lipid metabolism.
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