ORCID Profile
0000-0002-8018-3454
Current Organisation
University of South Australia
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Publisher: Elsevier BV
Date: 09-2020
Publisher: Wiley
Date: 11-06-2023
DOI: 10.1111/ECI.14037
Abstract: Cancer is a leading cause of morbidity and mortality worldwide, and better understanding of the risk factors could enhance prevention. We conducted a hypothesis‐free analysis combining machine learning and statistical approaches to identify cancer risk factors from 2828 potential predictors captured at baseline. There were 459,169 UK Biobank participants free from cancer at baseline and 48,671 new cancer cases during the 10‐year follow‐up. Logistic regression models adjusted for age, sex, ethnicity, education, material deprivation, smoking, alcohol intake, body mass index and skin colour (as a proxy for sun sensitivity) were used for obtaining adjusted odds ratios, with continuous predictors presented using quintiles (Q). In addition to smoking, older age and male sex, positively associating features included several anthropometric characteristics, whole body water mass, pulse, hypertension and biomarkers such as urinary microalbumin (Q5 vs. Q1 OR 1.16, 95% CI = 1.13–1.19), C‐reactive protein (Q5 vs. Q1 OR 1.20, 95% CI = 1.16–1.24) and red blood cell distribution width (Q5 vs. Q1 OR 1.18, 95% CI = 1.14–1.21), among others. High‐density lipoprotein cholesterol (Q5 vs. Q1 OR 0.84, 95% CI = 0.81–0.87) and albumin (Q5 vs. Q1 OR 0.84, 95% CI = 0.81–0.87) were inversely associated with cancer. In sex‐stratified analyses, higher testosterone increased the risk in females but not in males (Q5 vs. Q1 OR females 1.23, 95% CI = 1.17–1.30). Phosphate was associated with a lower risk in females but a higher risk in males (Q5 vs. Q1 OR females 0.94, 95% CI = 0.90–0.99 vs. OR males 1.09, 95% CI 1.04–1.15). This hypothesis‐free analysis suggests personal characteristics, metabolic biomarkers, physical measures and smoking as important predictors of cancer risk, with further studies needed to confirm causality and clinical relevance.
Publisher: Springer Science and Business Media LLC
Date: 23-04-2016
Publisher: Springer Science and Business Media LLC
Date: 21-06-2022
DOI: 10.1038/S42003-022-03554-Y
Abstract: Hormone-related cancers, including cancers of the breast, prostate, ovaries, uterine, and thyroid, globally contribute to the majority of cancer incidence. We hypothesize that hormone-sensitive cancers share common genetic risk factors that have rarely been investigated by previous genomic studies of site-specific cancers. Here, we show that considering hormone-sensitive cancers as a single disease in the UK Biobank reveals shared genetic aetiology. We observe that a significant proportion of variance in disease liability is explained by the genome-wide single nucleotide polymorphisms (SNPs), i.e., SNP-based heritability on the liability scale is estimated as 10.06% (SE 0.70%). Moreover, we find 55 genome-wide significant SNPs for the disease, using a genome-wide association study. Pair-wise analysis also estimates positive genetic correlations between some pairs of hormone-sensitive cancers although they are not statistically significant. Our finding suggests that heritable genetic factors may be a key driver in the mechanism of carcinogenesis shared by hormone-sensitive cancers.
Publisher: Hindawi Limited
Date: 14-10-2019
DOI: 10.1002/DA.22963
Abstract: This study aimed to explore the association between depression and body mass index (BMI), and to investigate whether genetic susceptibility to high BMI is different among in iduals with or without depression. We used data on 251,125 in iduals of white British ancestry from the UK Biobank. We conducted Mendelian randomization (MR) analysis to test for a causal association between depression and BMI using a major depressive disorder (MDD)-related genetic risk score (GRS We found observational and genetic evidence for an association between depression and BMI (MR beta: 0.09, 95% confidence interval [CI] 0.04-0.13). Further, the contribution of genetic risk to high BMI was higher among in iduals with depression compared to controls. Carrying 10 additional BMI increasing alleles was associated with 0.24 standard deviation (SD 95%CI 0.23-0.25) higher BMI among depressed in iduals compared to 0.20 SD (95%CI 0.19-0.21) higher in controls, which corresponds to 3.4 kg and 2.8 kg extra weight for an in idual of average height. Amongst the in idual loci, the evidence for interaction was most notable for a variant near MC4R, a gene known to affect both appetite regulation and the hypothalamic-pituitary adrenal axis (p Genetic predisposition to high BMI was higher among depressed than to nondepressed in iduals. This study provides support for a possible role of MC4R in the link between depression and obesity.
Publisher: Springer Science and Business Media LLC
Date: 21-05-2022
DOI: 10.1038/S41598-022-12198-1
Abstract: We assigned 329,908 UK Biobank participants into six subgroups based on a self-organizing map of 51 biochemical measures (blinded for clinical outcomes). The subgroup with the most favorable metabolic traits was chosen as the reference. Hazard ratios (HR) for incident disease were modeled by Cox regression. Enrichment ratios (ER) of incident multi-morbidity versus randomly expected co-occurrence were evaluated by permutation tests ER is like HR but captures co-occurrence rather than event frequency. The subgroup with high urinary excretion without kidney stress (HR = 1.24) and the subgroup with the highest apolipoprotein B and blood pressure (HR = 1.52) were associated with ischemic heart disease (IHD). The subgroup with kidney stress, high adiposity and inflammation was associated with IHD (HR = 2.11), cancer (HR = 1.29), dementia (HR = 1.70) and mortality (HR = 2.12). The subgroup with high liver enzymes and triglycerides was at risk of diabetes (HR = 15.6). Multimorbidity was enriched in metabolically favorable subgroups (3.4 ≤ ER ≤ 4.0) despite lower disease burden overall the relative risk of co-occurring disease was higher in the absence of obvious metabolic dysfunction. These results provide synergistic insight into metabolic health and its associations with cardiovascular disease in a large population s le.
Publisher: Elsevier BV
Date: 2022
Publisher: Elsevier BV
Date: 07-2019
Publisher: Oxford University Press (OUP)
Date: 29-11-2022
DOI: 10.1093/GBE/EVAC167
Abstract: Variation in genes involved in the absorption, distribution, metabolism, and excretion of drugs (ADME) can influence in idual response to a therapeutic treatment. The study of ADME genetic ersity in human populations has led to evolutionary hypotheses of adaptation to distinct chemical environments. Population differentiation in measured drug metabolism phenotypes is, however, scarcely documented, often indirectly estimated via genotype-predicted phenotypes. We administered seven probe compounds devised to target six cytochrome P450 enzymes and the P-glycoprotein (P-gp) activity to assess phenotypic variation in four populations along a latitudinal transect spanning over Africa, the Middle East, and Europe (349 healthy Ethiopian, Omani, Greek, and Czech volunteers). We demonstrate significant population differentiation for all phenotypes except the one measuring CYP2D6 activity. Genome-wide association studies (GWAS) evidenced that the variability of phenotypes measuring CYP2B6, CYP2C9, CYP2C19, and CYP2D6 activity was associated with genetic variants linked to the corresponding encoding genes, and additional genes for the latter three. Instead, GWAS did not indicate any association between genetic ersity and the phenotypes measuring CYP1A2, CYP3A4, and P-gp activity. Genome scans of selection highlighted multiple candidate regions, a few of which included ADME genes, but none overlapped with the GWAS candidates. Our results suggest that different mechanisms have been shaping the evolution of these phenotypes, including phenotypic plasticity, and possibly some form of balancing selection. We discuss how these contrasting results highlight the erse evolutionary trajectories of ADME genes and proteins, consistent with the wide spectrum of both endogenous and exogenous molecules that are their substrates.
Publisher: Informa UK Limited
Date: 24-06-2022
Publisher: Wiley
Date: 17-11-2022
DOI: 10.1002/ACR.24884
Abstract: In this Mendelian randomization (MR) study, the objective was to investigate the causal effect of metabolically different adiposity subtypes on osteoarthritis. We performed 2‐s le MR using summary‐level data for osteoarthritis (10,083 cases and 40,425 controls) from a genome‐wide association using the UK Biobank, and for site‐specific osteoarthritis from the Arthritis Research UK Osteoarthritis Genetics consortium. We used 3 classes of genetic instruments, which all increase body mass index but are associated with different metabolic profiles (unfavorable, neutral, and favorable). Primary analysis was performed using inverse variance weight (IVW), with additional sensitivity analysis from different MR methods. We further applied a nonlinear MR using UK Biobank data to understand the nature of the adiposity–osteoarthritis relationship. Greater metabolically unfavorable and metabolically neutral adiposity were associated with higher odds of osteoarthritis (IVW odds ratio [OR] 1.56 [95% confidence interval (95% CI) 1.31, 1.85] and OR 1.60 [95% CI 1.15, 2.23], respectively). The estimate for the association between metabolically favorable adiposity and osteoarthritis was similar, although with notable imprecision (OR 1.55 [95% CI 0.70, 3.41]). Using site‐specific osteoarthritis, metabolically unfavorable, neutral, and favorable adiposity were all associated with higher odds of knee osteoarthritis (OR 1.44 [95% CI 1.04, 1.98], OR 2.28 [95% CI 1.04, 4.99], and OR 6.80 [95% CI 2.08, 22.19], respectively). We found generally consistent estimates with a wider confidence interval crossing the null from other MR methods. The nonlinear MR analyses suggested a nonlinear relationship between metabolically unfavorable adiposity and osteoarthritis ( P nonlinear = 0.003). Metabolic abnormalities did not explain the association between greater adiposity and the risk of osteoarthritis, which might suggest that the association is largely due to a mechanical effect on the joints.
Publisher: Sciencedomain International
Date: 10-01-2014
Publisher: Springer Science and Business Media LLC
Date: 23-11-2016
Publisher: MDPI AG
Date: 21-09-2022
DOI: 10.3390/NU14193907
Abstract: Genetic susceptibility and lifestyle affect the risk of dementia but there is little direct evidence for their associations with preclinical changes in brain structure. We investigated the association of genetic dementia risk and healthy lifestyle with brain morphometry, and whether effects from elevated genetic risk are modified by lifestyle changes. We used prospective data from up to 25,894 UK Biobank participants (median follow-up of 8.8 years), and defined healthy lifestyle according to American Heart Association criteria as BMI 30, no smoking, healthy diet and regular physical activity). Higher genetic risk was associated with lower hippoc al volume (beta −0.16 cm3, 95% CI −0.22, −0.11) and total brain volume (−4.34 cm3, 95% CI −7.68, −1.01) in participants aged ≥60 years but not years. Healthy lifestyle was associated with higher total brain, grey matter and hippoc al volumes, and lower volume of white matter hyperintensities, with no effect modification by age or genetic risk. In conclusion, adverse effects of high genetic risk on brain health were only found in older participants, while adhering to healthy lifestyle recommendations is beneficial regardless of age or genetic risk.
Publisher: Springer Science and Business Media LLC
Date: 17-09-2018
Publisher: Springer Science and Business Media LLC
Date: 27-08-2021
DOI: 10.1038/S41366-021-00942-Y
Abstract: Observational and Mendelian randomization (MR) studies link obesity and cancer, but it remains unclear whether these depend upon related metabolic abnormalities. We used information from 321,472 participants in the UK biobank, including 30,561 cases of obesity-related cancer. We constructed three genetic instruments reflecting higher adiposity together with either "unfavourable" (82 SNPs), "favourable" (24 SNPs) or "neutral" metabolic profile (25 SNPs). We looked at associations with 14 types of cancer, previously suggested to be associated with obesity. All genetic instruments had a strong association with BMI (p < 1 × 10 Higher adiposity associated with a higher risk of non-hormonal cancer but a lower risk of some hormone related cancers. Presence of metabolic abnormalities might aggravate the adverse effects of higher adiposity on cancer. Further studies are warranted to investigate whether interventions on adverse metabolic health may help to alleviate obesity-related cancer risk.
Publisher: Elsevier BV
Date: 06-2023
Publisher: MDPI AG
Date: 30-12-2020
DOI: 10.3390/NU13010109
Abstract: The relationship between depression and vitamin D deficiency is complex, with evidence mostly from studies affected by confounding and reverse causality. We examined the causality and direction of the relationship between 25-hydroxyvitamin D (25(OH)D) and depression in bi-directional Mendelian randomization (MR) analyses using information from up to 307,618 white British participants from the UK Biobank and summary results from the SUNLIGHT (n = 79,366) and Psychiatric Genomics consortia (PGC 113,154 cases and 218,523 controls). In observational analysis, the odds of depression decreased with higher 25(OH)D concentrations (adjusted odds ratio (OR) per 50% increase 0.95, 95%CI 0.94–0.96). In MR inverse variance weighted (IVW) using the UK Biobank, there was no association between genetically determined serum 25(OH)D and depression (OR per 50% higher 0.97, 95%CI 0.90–1.05) with consistent null association across all MR approaches and in data from PGC consortium. In contrast, genetic liability to depression was associated with lower 25(OH)D concentrations (MR IVW −3.26%, −4.94%–−1.55%), with the estimates remaining generally consistent after meta-analysing with the consortia. In conclusion, we found genetic evidence for a causal effect of depression on lower 25(OH)D concentrations, however we could not confirm a beneficial effect of nutritional vitamin D status on depression risk.
Publisher: Oxford University Press (OUP)
Date: 21-02-2022
Abstract: There has been an increased interest in health technology assessment and economic evaluations for health policy in Ethiopia over the last few years. In this systematic review, we examined the scope and quality of healthcare economic evaluation studies in Ethiopia. We searched seven electronic databases (PubMed/MEDLINE, EMBASE, PsycINFO, CINHAL, Econlit, York CRD databases and CEA Tufts) from inception to May 2021 to identify published full health economic evaluations of a health-related intervention or programme in Ethiopia. This was supplemented with forward and backward citation searches of included articles, manual search of key government websites, the Disease Control Priorities-Ethiopia project and WHO-CHOICE programme. The quality of reporting of economic evaluations was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. The extracted data were grouped into subcategories based on the subject of the economic evaluation, organized into tables and reported narratively. This review identified 34 full economic evaluations conducted between 2009 and 2021. Around 14 (41%) of studies focussed on health service delivery, 8 (24%) on pharmaceuticals, vaccines and devices, and 4 (12%) on public-health programmes. The interventions were mostly preventive in nature and focussed on communicable diseases (n = 19 56%) and maternal and child health (n = 6 18%). Cost-effectiveness ratios varied widely from cost-saving to more than US $37 313 per life saved depending on the setting, perspectives, types of interventions and disease conditions. While the overall quality of included studies was judged as moderate (meeting 69% of CHEERS checklist), only four out of 27 cost-effectiveness studies characterized heterogeneity. There is a need for building local technical capacity to enhance the design, conduct and reporting of health economic evaluations in Ethiopia.
Publisher: Wiley
Date: 2021
DOI: 10.1002/TRC2.12200
Abstract: Dementia is currently one of the leading causes of mortality globally, and mortality due to dementia will likely increase in the future along with corresponding increases in population growth and population aging. However, large inconsistencies in coding practices in vital registration systems over time and between countries complicate the estimation of global dementia mortality. We meta‐analyzed the excess risk of death in those with dementia and multiplied these estimates by the proportion of dementia deaths occurring in those with severe, end‐stage disease to calculate the total number of deaths that could be attributed to dementia. We estimated that there were 1.62 million (95% uncertainty interval [UI]: 0.41–4.21) deaths globally due to dementia in 2019. More dementia deaths occurred in women (1.06 million [0.27–2.71]) than men (0.56 million [0.14–1.51]), largely but not entirely due to the higher life expectancy in women (age‐standardized female‐to‐male ratio 1.19 [1.10–1.26]). Due to population aging, there was a large increase in all‐age mortality rates from dementia between 1990 and 2019 (100.1% [89.1–117.5]). In 2019, deaths due to dementia ranked seventh globally in all ages and fourth among in iduals 70 and older compared to deaths from other diseases estimated in the Global Burden of Disease (GBD) study. Mortality due to dementia represents a substantial global burden, and is expected to continue to grow into the future as an older, aging population expands globally.
Publisher: Wiley
Date: 06-06-2023
DOI: 10.1111/BCP.15793
Abstract: Lipid‐lowering medications are widely used to control blood cholesterol levels and manage a range of cardiovascular and lipid disorders. We aimed to explore the possible associations between LDL lowering and multiple disease outcomes or biomarkers. We performed a Mendelian randomization phenome‐wide association study (MR‐PheWAS) in 337 475 UK Biobank participants to test for associations between four proposed LDL‐C‐lowering genetic risk scores ( PCSK9 , HMGCR , NPC1L1 and LDLR ) and 1135 disease outcomes, with follow‐up MR analyses in 52 serum, urine, imaging and clinical biomarkers. We used inverse‐variance weighted MR in the main analyses and complementary MR methods (weighted median, weighted mode, MR‐Egger and MR‐PRESSO) as sensitivity analyses. We accounted for multiple testing with false discovery rate correction ( P 2.0 × 10 −4 for phecodes, P 1.3 × 10 −2 for biomarkers). We found evidence for an association between genetically instrumented LDL lowering and 10 distinct disease outcomes, suggesting potential causality. All genetic instruments were associated with hyperlipidaemias and cardiovascular diseases in the expected directions. Biomarker analyses supported an effect of LDL‐C lowering through PCSK9 on lung function (FEV [beta per 1 mg/dL lower LDL‐C −1.49, 95% CI −2.21, −0.78] FVC [−1.42, 95% CI −2.29, −0.54]) and through HMGCR on hippoc al volume (beta per 1 mg/dL lower LDL‐C 6.09, 95% CI 1.74, 10.44). We found genetic evidence to support both positive and negative effects of LDL‐C lowering through all four LDL‐C‐lowering pathways. Future studies should further explore the effects of LDL‐C lowering on lung function and changes in brain volume.
Publisher: Springer Science and Business Media LLC
Date: 09-11-2015
Publisher: International Union Against Tuberculosis and Lung Disease
Date: 21-12-2014
DOI: 10.5588/PHA.14.0054
Publisher: Elsevier BV
Date: 06-2021
DOI: 10.1016/J.NEUROBIOLAGING.2021.02.010
Abstract: To establish causal evidence for the association of adiposity-related metabolic abnormalities with brain volumes, and the risks of dementia and stroke, we applied 1- and 2-s le Mendelian randomization (MR) analyses using up to 336,309 UK Biobank participants. We used 3 classes of genetic instruments, which all increase body mass index but are associated with different metabolic profiles (unfavorable, neutral and favorable). We validated the instruments using anthropometric and cardio-metabolic traits. Both metabolically unfavorable and metabolically neutral adiposity associated with lower gray matter volume (GMV, -9.28 cm
Publisher: Wiley
Date: 21-09-2023
DOI: 10.1111/DOM.14853
Abstract: To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia. Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the associations of metabolic subgroups, derived using an artificial neural network approach (self‐organizing map), and 39 in idual biomarkers with brain MRI measures: total brain volume (TBV), grey matter volume (GMV), white matter volume (WMV), hippoc al volume (HV), white matter hyperintensity (WMH) volume, and caudate iron deposition. In metabolic subgroup analyses, participants characterized by high triglycerides and liver enzymes showed the most adverse brain outcomes compared to the healthy reference subgroup with high‐density lipoprotein cholesterol and low body mass index (BMI) including associations with GMV ( β standardized −0.20, 95% confidence interval [CI] −0.24 to −0.16), HV ( β standardized −0.09, 95% CI −0.13 to −0.04), WMH volume ( β standardized 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition ( β standardized 0.30, 95% CI 0.25 to 0.34), with similar adverse associations for the subgroup with high BMI, C‐reactive protein and cystatin C, and the subgroup with high blood pressure (BP) and apolipoprotein B. Among the biomarkers, striking associations were seen between basal metabolic rate (BMR) and caudate iron deposition ( β standardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV ( β standardized −0.15, 95% CI −0.16 to −0.14) and HV ( β standardized −0.11, 95% CI −0.12 to −0.10), and between BP and WMH volume ( β standardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP). Metabolic profiles were associated differentially with brain neuroimaging characteristics. Associations of BMR, BP and other in idual biomarkers may provide insights into actionable mechanisms driving these brain associations.
Publisher: Informa UK Limited
Date: 12-2016
Publisher: Springer Science and Business Media LLC
Date: 11-12-2021
DOI: 10.1186/S12888-021-03631-2
Abstract: Globally, the prevalence of metabolic syndrome (MetS) is higher among patients with schizophrenia than the general population, and this leads to higher morbidity and mortality in this population. The aim of this study was to investigate the MetS prevalence among patients with schizophrenia in Ethiopia. We conducted a cross-sectional analysis of baseline data of 200 patients with schizophrenia recruited from Amanuel Mental Specialized Hospital, Addis Ababa, Ethiopia. Lipid profile and blood glucose levels were measured using Roche Cobas 6000 clinical chemistry analyzer. The prevalence of MetS was assessed based on National Cholesterol Education Program Adult Treatment Panel III criteria. Patients’ demographic information, clinical and laboratory data, lifestyle habits, particularly smoking and Khat chewing, were evaluated vis-à-vis MetS. The overall prevalence of MetS in patients with schizophrenia was 21.5% (17.1% male, 29.6% female) where Low HDL-cholesterol value was the most common metabolic disorders components in both males and females subgroups. In the multivariate analysis, the positive and negative symptoms score (PANSS, AOR = 1.03, 95% CI 1.001–1.054) was associated factors with MetS. In Ethiopia, patients with schizophrenia were found to have higher prevalence of MetS than the general population. Physicians/health care providers should routinely screen patients with schizophrenia for MetS and initiate timely management of those who develop the syndrome to reduce the health cost from caring for NCDs, improve the patients’ quality of life, and prevent premature mortality.
Publisher: Elsevier BV
Date: 08-2022
DOI: 10.1093/AJCN/NQAC107
Publisher: Elsevier BV
Date: 2023
DOI: 10.1016/J.METABOL.2022.155342
Abstract: Analyses to predict the risk of cancer typically focus on single biomarkers, which do not capture their complex interrelations. We hypothesized that the use of metabolic profiles may provide new insights into cancer prediction. We used information from 290,888 UK Biobank participants aged 37 to 73 years at baseline. Metabolic subgroups were defined based on clustering of biochemical data using an artificial neural network approach and examined for their association with incident cancers identified through linkage to cancer registry. In addition, we evaluated associations between 38 in idual biomarkers and cancer risk. In total, 21,973 in iduals developed cancer during the follow-up (median 3.87 years, interquartile range [IQR] = 2.03-5.58). Compared to the metabolically favorable subgroup (IV), subgroup III (defined as "high BMI, C-reactive protein & cystatin C") was associated with a higher risk of obesity-related cancers (hazard ratio [HR] = 1.26, 95 % CI = 1.21 to 1.32) and hematologic-malignancies (e.g., lymphoid leukemia: HR = 1.83, 95%CI = 1.44 to 2.33). Subgroup II ("high triglycerides & liver enzymes") was strongly associated with liver cancer risk (HR = 5.70, 95%CI = 3.57 to 9.11). Analysis of in idual biomarkers showed a positive association between testosterone and greater risks of hormone-sensitive cancers (HR per SD higher = 1.32, 95%CI = 1.23 to 1.44), and liver cancer (HR = 2.49, 95%CI =1.47 to 4.24). Many liver tests were in idually associated with a greater risk of liver cancer with the strongest association observed for gamma-glutamyl transferase (HR = 2.40, 95%CI = 2.19 to 2.65). Metabolic profile in middle-to-older age can predict cancer incidence, in particular risk of obesity-related cancer, hematologic malignancies, and liver cancer. Elevated values from liver tests are strong predictors for later risk of liver cancer.
Publisher: Springer Science and Business Media LLC
Date: 19-08-2020
DOI: 10.1038/S41380-019-0486-1
Abstract: Depression affects all aspects of an in idual's life but evidence relating to the causal effects on health is limited. We used information from 337,536 UK Biobank participants and performed hypothesis-free phenome-wide association analyses between major depressive disorder (MDD) genetic risk score (GRS) and 925 disease outcomes. GRS-disease outcome associations passing the multiple-testing corrected significance threshold (P < 1.9 × 10
Publisher: Cold Spring Harbor Laboratory
Date: 08-09-2023
Publisher: Elsevier BV
Date: 09-2020
Publisher: Springer Science and Business Media LLC
Date: 26-08-2020
Publisher: Cold Spring Harbor Laboratory
Date: 03-02-2021
DOI: 10.1101/2021.02.01.21250893
Abstract: Background: Ischemic heart disease (IHD), diabetes, cancer and dementia share features of age-associated metabolic dysfunction. We hypothesized that metabolic ersity explains the ersity of morbidity later in life. Methods: We analyzed data from the UK Biobank (N = 329,908). A self-organizing map (SOM, an artificial neural network) was trained with 51 metabolic traits adjusted for age and sex. The SOM analyses produced six subgroups that summarized the multi-variable metabolic ersity. The subgroup with the lowest adiposity and disease burden was chosen as the reference. Hazard ratios (HR) were modeled by Cox regression (P 0.0001 unless otherwise indicated). Enrichment of multi-morbidity over random expectation was tested by permutation analysis. Results: The subgroup with the highest sex hormones was not associated with IHD (HR = 1.04, P = 0.14). The subgroup with high urinary excretion without kidney stress (HR = 1.24) and the subgroup with the highest apolipoprotein B and blood pressure (HR = 1.52) were associated with IHD. The subgroup with high adiposity, inflammation and kidney stress was associated with IHD (HR = 2.11), cancer (HR= 1.29), dementia (HR = 1.70) and mortality (HR = 2.12). The subgroup with high triglycerides and liver enzymes was at risk of diabetes (HR = 15.6). Paradoxical enrichment of multimorbidity in young in iduals and in favorable subgroups was observed. Conclusions: These results support metabolic ersity as an explanation to erging morbidity and demonstrate the potential value of population-based metabolic subgroups as public health targets for reducing aggregate burden of chronic diseases in ageing populations.
No related grants have been discovered for Anwar Mulugeta Gebremichael.