Inflammation-Related Marker Profiling of Dietary Patterns and All-cause Mortality in the Melbourne Collaborative Cohort Study.

BACKGROUND: Nutritional epidemiology research using self-reported dietary intake is prone to measurement error. Objective methods are being explored to overcome this limitation. OBJECTIVES: We aimed to examine 1) the association between plasma markers related to inflammation and derive marker scores for dietary patterns [Mediterranean dietary score (MDS), energy-adjusted Dietary Inflammatory Index (E-DIITM), Alternative Healthy Eating Index 2010 (AHEI)] and 2) the associations of these marker scores with mortality. METHODS: Weighted marker scores were derived from the cross-sectional association between 30 plasma markers and each dietary score (assessed using food-frequency questionnaires) using linear regression for 770 participants in the Melbourne Collaborative Cohort Study (aged 50-82 y). Prospective associations between marker scores and mortality (n = 249 deaths) were assessed using Cox regression (median follow-up: 14.4 y). RESULTS: The MDS, E-DII, and AHEI were associated (P < 0.05) with 9, 14, and 11 plasma markers, respectively. Healthier diets (higher MDS and AHEI, and lower anti-inflammatory, E-DII) were associated with lower concentrations of kynurenines, neopterin, IFN-γ, cytokines, and C-reactive protein. Five of 6 markers common to the 3 dietary scores were components of the kynurenine pathway. The 3 dietary-based marker scores were highly correlated (Spearman ρ: -0.74, -0.82, and 0.93). Inverse associations (for 1-SD increment) were observed with all-cause mortality for the MDS marker score (HR: 0.84; 95% CI: 0.72-0.98) and the AHEI marker score (HR: 0.76; 95% CI: 0.66-0.89), whereas a positive association was observed with the E-DII marker score (HR: 1.18; 95% CI: 1.01-1.39). The same magnitude of effect was not observed for the respective dietary patterns. CONCLUSIONS: Markers involved in inflammation-related processes are associated with dietary quality, including a substantial overlap between markers associated with the MDS, the E-DII, and the AHEI, especially kynurenines. Unfavorable marker scores, reflecting poorer-quality diets, were associated with increased mortality.

Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium.

Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P < 1 × 10-6) associated with intake of any macronutrient in 91,114 European ancestry participants. Four loci replicated and reached genome-wide significance in a combined meta-analysis including 123,659 European descent participants, unraveling two novel loci; a common variant in RARB locus for carbohydrate intake and a rare variant in DRAM1 locus for protein intake, and corroborating earlier FGF21 and FTO findings. In additional analysis of 144,770 participants from the UK Biobank, all identified associations from the two-stage analysis were confirmed except for DRAM1. Identified loci might have implications in brain and adipose tissue biology and have clinical impact in obesity-related phenotypes. Our findings provide new insight into biological functions related to macronutrient intake.

Interplay between genetic predisposition, macronutrient intake and type 2 diabetes incidence: analysis within EPIC-InterAct across eight European countries.

AIMS/HYPOTHESIS: Gene-macronutrient interactions may contribute to the development of type 2 diabetes but research evidence to date is inconclusive. We aimed to increase our understanding of the aetiology of type 2 diabetes by investigating potential interactions between genes and macronutrient intake and their association with the incidence of type 2 diabetes. METHODS: We investigated the influence of interactions between genetic risk scores (GRSs) for type 2 diabetes, insulin resistance and BMI and macronutrient intake on the development of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct, a prospective case-cohort study across eight European countries (N = 21,900 with 9742 incident type 2 diabetes cases). Macronutrient intake was estimated from diets reported in questionnaires, including proportion of energy derived from total carbohydrate, protein, fat, plant and animal protein, saturated, monounsaturated and polyunsaturated fat and dietary fibre. Using multivariable-adjusted Cox regression, we estimated country-specific interaction results on the multiplicative scale, using random-effects meta-analysis. Secondary analysis used isocaloric macronutrient substitution. RESULTS: No interactions were identified between any of the three GRSs and any macronutrient intake, with low-to-moderate heterogeneity between countries (I2 range 0-51.6%). Results were similar using isocaloric macronutrient substitution analyses and when weighted and unweighted GRSs and individual SNPs were examined. CONCLUSIONS/INTERPRETATION: Genetic susceptibility to type 2 diabetes, insulin resistance and BMI did not modify the association between macronutrient intake and incident type 2 diabetes. This suggests that macronutrient intake recommendations to prevent type 2 diabetes do not need to account for differences in genetic predisposition to these three metabolic conditions.

The effect of communicating the genetic risk of cardiometabolic disorders on motivation and actual engagement in preventative lifestyle modification and clinical outcome: a systematic review and meta-analysis of randomised controlled trials.

Genetic risk prediction of chronic conditions including obesity, diabetes and CVD currently has limited predictive power but its potential to engage healthy behaviour change has been of immense research interest. We aimed to understand whether the latter is indeed true by conducting a systematic review and meta-analysis investigating whether genetic risk communication affects motivation and actual behaviour change towards preventative lifestyle modification. We included all randomised controlled trials (RCT) since 2003 investigating the impact of genetic risk communication on health behaviour to prevent cardiometabolic disease, without restrictions on age, duration of intervention or language. We conducted random-effects meta-analyses for perceived motivation for behaviour change and clinical changes (weight loss) and a narrative analysis for other outcomes. Within the thirteen studies reviewed, five were vignette studies (hypothetical RCT) and seven were clinical RCT. There was no consistent effect of genetic risk on actual motivation for weight loss, perceived motivation for dietary change (control v. genetic risk group standardised mean difference (smd) -0·15; 95 % CI -1·03, 0·73, P=0·74) or actual change in dietary behaviour. Similar results were observed for actual weight loss (control v. high genetic risk SMD 0·29 kg; 95 % CI -0·74, 1·31, P=0·58). This review found no clear or consistent evidence that genetic risk communication alone either raises motivation or translates into actual change in dietary intake or physical activity to reduce the risk of cardiometabolic disorders in adults. Of thirteen studies, eight were at high or unclear risk of bias. Additional larger-scale, high-quality clinical RCT are warranted.

Adiposity and plasma concentrations of kynurenine pathway metabolites and traditional markers of inflammation.

AIM: The kynurenine pathway is increasingly recognised to play a role in inflammation and disease. We assessed the cross-sectional and longitudinal associations of adiposity measures (body mass index, waist-hip ratio, waist circumference and fat mass ratio) with plasma concentrations of kynurenine pathway metabolites and traditional markers of inflammation. METHODS: We used data from 970 Melbourne Collaborative Cohort Study participants who had plasma markers measured at baseline (median age 59 years) and follow-up (median age 70 years). Linear regression was used to assess cross-sectional and longitudinal associations between four adiposity measures and concentrations of i) nine kynurenine pathway metabolites; ii) two derived markers; iii) eight traditional inflammatory markers. RESULTS: Cross-sectionally, most kynurenine metabolites were strongly associated with adiposity measures at both time points; associations were generally stronger than for most inflammation markers except CRP (e.g. body mass index at baseline, quinolinic acid (per S.D. ß = 0.30, 95%CI: 0.24-0.36, P = 10-21), kynurenine (ß = 0.25, 95%CI: 0.19-0.31, P = 10-16) and CRP (ß = 0.31, 95%CI: 0.25-0.37, P = 10-24), and remained largely unchanged after adjustment for confounders. Longitudinally, changes in adiposity measures over approximately a decade were positively associated with changes in kynurenine metabolite concentrations (in particular for 3-hydroxyanthranilic acid, kynurenine and quinolinic acid), and more strongly so than for other markers of inflammation, including CRP. CONCLUSIONS: In middle-aged and older adults, plasma concentrations of kynurenine metabolites are strongly associated with adiposity, both cross-sectionally and longitudinally. Our study demonstrates that kynurenine metabolites may be valuable markers to monitor the adverse consequences of obesity.

Inflammation and Epigenetic Aging Are Largely Independent Markers of Biological Aging and Mortality.

Limited evidence exists on the link between inflammation and epigenetic aging. We aimed to (a) assess the cross-sectional and prospective associations of 22 inflammation-related plasma markers and a signature of inflammaging with epigenetic aging and (b) determine whether epigenetic aging and inflammaging are independently associated with mortality. Blood samples from 940 participants in the Melbourne Collaborative Cohort Study collected at baseline (1990-1994) and follow-up (2003-2007) were assayed for DNA methylation and 22 inflammation-related markers, including well-established markers (eg, interleukins and C-reactive protein) and metabolites of the tryptophan-kynurenine pathway. Four measures of epigenetic aging (PhenoAge, GrimAge, DunedinPoAm, and Zhang) and a signature of inflammaging were considered, adjusted for age, and transformed to Z scores. Associations were assessed using linear regression, and mortality hazard ratios (HR) and 95% confidence intervals (95% CI) were estimated using Cox regression. Cross-sectionally, most inflammation-related markers were associated with epigenetic aging measures, although with generally modest effect sizes (regression coefficients per SD ≤ 0.26) and explaining altogether between 1% and 11% of their variation. Prospectively, baseline inflammation-related markers were not, or only weakly, associated with epigenetic aging after 11 years of follow-up. Epigenetic aging and inflammaging were strongly and independently associated with mortality, for example, inflammaging: HR = 1.41, 95% CI = 1.27-1.56, p = 2 × 10-10, which was only slightly attenuated after adjustment for 4 epigenetic aging measures: HR = 1.35, 95% CI = 1.22-1.51, p = 7 × 10-9). Although cross-sectionally associated with epigenetic aging, inflammation-related markers accounted for a modest proportion of its variation. Inflammaging and epigenetic aging are essentially nonoverlapping markers of biological aging and may be used jointly to predict mortality.

Association of Markers of Inflammation, the Kynurenine Pathway and B Vitamins with Age and Mortality, and a Signature of Inflammaging.

BACKGROUND: Inflammation is a key feature of aging. We aimed to (i) investigate the association of 34 blood markers potentially involved in inflammatory processes with age and mortality and (ii) develop a signature of "inflammaging." METHODS: Thirty-four blood markers relating to inflammation, B vitamin status, and the kynurenine pathway were measured in 976 participants in the Melbourne Collaborative Cohort Study at baseline (median age = 59 years) and follow-up (median age = 70 years). Associations with age and mortality were assessed using linear and Cox regression, respectively. A parsimonious signature of inflammaging was developed and its association with mortality was compared with 2 marker scores calculated across all markers associated with age and mortality, respectively. RESULTS: The majority of markers (30/34) were associated with age, with stronger associations observed for neopterin, cystatin C, interleukin (IL)-6, tumor necrosis factor alpha (TNF-α), several markers of the kynurenine pathway and derived indices KTR (kynurenine/tryptophan ratio), PAr index (ratio of 4-pyridoxic acid and the sum of pyridoxal 5'-phosphate and pyridoxal), and HK:XA (3-hydroxykynurenine/xanthurenic acid ratio). Many markers (17/34) showed an association with mortality, in particular IL-6, neopterin, C-reactive protein, quinolinic acid, PAr index, and KTR. The inflammaging signature included 10 markers and was strongly associated with mortality (hazard ratio [HR] per SD = 1.40, 95% CI: 1.24-1.57, p = 2 × 10-8), similar to scores based on all age-associated (HR = 1.38, 95% CI: 1.23-1.55, p = 4 × 10-8) and mortality-associated markers (HR = 1.43, 95% CI: 1.28-1.60, p = 1 × 10-10), respectively. Strong evidence of replication of the inflammaging signature association with mortality was found in the Hordaland Health Study. CONCLUSION: Our study highlights the key role of the kynurenine pathway and vitamin B6 catabolism in aging, along with other well-established inflammation-related markers. A signature of inflammaging based on 10 markers was strongly associated with mortality.

Prospective Evaluation over 15 Years of Six Breast Cancer Risk Models.

Prospective validation of risk models is needed to assess their clinical utility, particularly over the longer term. We evaluated the performance of six commonly used breast cancer risk models (IBIS, BOADICEA, BRCAPRO, BRCAPRO-BCRAT, BCRAT, and iCARE-lit). 15-year risk scores were estimated using lifestyle factors and family history measures from 7608 women in the Melbourne Collaborative Cohort Study who were aged 50-65 years and unaffected at commencement of follow-up two (conducted in 2003-2007), of whom 351 subsequently developed breast cancer. Risk discrimination was assessed using the C-statistic and calibration using the expected/observed number of incident cases across the spectrum of risk by age group (50-54, 55-59, 60-65 years) and family history of breast cancer. C-statistics were higher for BOADICEA (0.59, 95% confidence interval (CI) 0.56-0.62) and IBIS (0.57, 95% CI 0.54-0.61) than the other models (p-difference ≤ 0.04). No model except BOADICEA calibrated well across the spectrum of 15-year risk (p-value < 0.03). The performance of BOADICEA and IBIS was similar across age groups and for women with or without a family history. For middle-aged Australian women, BOADICEA and IBIS had the highest discriminatory accuracy of the six risk models, but apart from BOADICEA, no model was well-calibrated across the risk spectrum.

Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models.

Background: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm and the International Breast Cancer Intervention Study breast cancer risk models are used to provide advice on screening intervals and chemoprevention. We evaluated the performance of these models, which now incorporate polygenic risk scores (PRSs), using a prospective cohort study. Methods: We used a case-cohort design, involving women in the Melbourne Collaborative Cohort Study aged 50-75 years when surveyed in 2003-2007, of whom 408 had a first primary breast cancer diagnosed within 10 years (cases), and 2783 were from the subcohort. Ten-year risks were calculated based on lifestyle factors, family history data, and a 313-variant PRS. Discrimination was assessed using a C-statistic compared with 0.50 and calibration using the ratio of expected to observed number of cases (E/O). Results: When the PRS was added to models with lifestyle factors and family history, the C-statistic (95% confidence interval [CI]) increased from 0.57 (0.54 to 0.60) to 0.62 (0.60 to 0.65) using IBIS and from 0.56 (0.53 to 0.59) to 0.62 (0.59 to 0.64) using BOADICEA. IBIS underpredicted risk (E/O = 0.62, 95% CI = 0.48 to 0.80) for women in the lowest risk category (<1.7%) and overpredicted risk (E/O = 1.40, 95% CI = 1.18 to 1.67) in the highest risk category (≥5%), using the Hosmer-Lemeshow test for calibration in quantiles of risk and a 2-sided P value less than .001. BOADICEA underpredicted risk (E/O = 0.82, 95% CI = 0.67 to 0.99) in the second highest risk category (3.4%-5%); the Hosmer-Lemeshow test and a 2-sided P value was equal to .02. Conclusions: Although the inclusion of a 313 genetic variant PRS doubles discriminatory accuracy (relative to reference 0.50), models with and without this PRS have relatively modest discrimination and might require recalibration before their clinical and wider use are promoted.

Recommended Definitions of Aggressive Prostate Cancer for Etiologic Epidemiologic Research.

BACKGROUND: In the era of widespread prostate-specific antigen testing, it is important to focus etiologic research on the outcome of aggressive prostate cancer, but studies have defined this outcome differently. We aimed to develop an evidence-based consensus definition of aggressive prostate cancer using clinical features at diagnosis for etiologic epidemiologic research. METHODS: Among prostate cancer cases diagnosed in 2007 in the National Cancer Institute's Surveillance, Epidemiology, and End Results-18 database with follow-up through 2017, we compared the performance of categorizations of aggressive prostate cancer in discriminating fatal prostate cancer within 10 years of diagnosis, placing the most emphasis on sensitivity and positive predictive value (PPV). RESULTS: In our case population (n = 55 900), 3073 men died of prostate cancer within 10 years. Among 12 definitions that included TNM staging and Gleason score, sensitivities ranged from 0.64 to 0.89 and PPVs ranged from 0.09 to 0.23. We propose defining aggressive prostate cancer as diagnosis of category T4 or N1 or M1 or Gleason score of 8 or greater prostate cancer, because this definition had one of the higher PPVs (0.23, 95% confidence interval = 0.22 to 0.24) and reasonable sensitivity (0.66, 95% confidence interval = 0.64 to 0.67) for prostate cancer death within 10 years. Results were similar across sensitivity analyses. CONCLUSIONS: We recommend that etiologic epidemiologic studies of prostate cancer report results for this definition of aggressive prostate cancer. We also recommend that studies separately report results for advanced category (T4 or N1 or M1), high-grade (Gleason score ≥8), and fatal prostate cancer. Use of this comprehensive set of endpoints will facilitate comparison of results from different studies and help elucidate prostate cancer etiology.

Interaction between genes and macronutrient intake on the risk of developing type 2 diabetes: systematic review and findings from European Prospective Investigation into Cancer (EPIC)-InterAct.

Background: Gene-diet interactions have been reported to contribute to the development of type 2 diabetes (T2D). However, to our knowledge, few examples have been consistently replicated to date.Objective: We aimed to identify existing evidence for gene-macronutrient interactions and T2D and to examine the reported interactions in a large-scale study.Design: We systematically reviewed studies reporting gene-macronutrient interactions and T2D. We searched the MEDLINE, Human Genome Epidemiology Network, and WHO International Clinical Trials Registry Platform electronic databases to identify studies published up to October 2015. Eligibility criteria included assessment of macronutrient quantity (e.g., total carbohydrate) or indicators of quality (e.g., dietary fiber) by use of self-report or objective biomarkers of intake. Interactions identified in the review were subsequently examined in the EPIC (European Prospective Investigation into Cancer)-InterAct case-cohort study (n = 21,148, with 9403 T2D cases; 8 European countries). Prentice-weighted Cox regression was used to estimate country-specific HRs, 95% CIs, and P-interaction values, which were then pooled by random-effects meta-analysis. A primary model was fitted by using the same covariates as reported in the published studies, and a second model adjusted for additional covariates and estimated the effects of isocaloric macronutrient substitution.Results: Thirteen observational studies met the eligibility criteria (n < 1700 cases). Eight unique interactions were reported to be significant between macronutrients [carbohydrate, fat, saturated fat, dietary fiber, and glycemic load derived from self-report of dietary intake and circulating n-3 (ω-3) polyunsaturated fatty acids] and genetic variants in or near transcription factor 7-like 2 (TCF7L2), gastric inhibitory polypeptide receptor (GIPR), caveolin 2 (CAV2), and peptidase D (PEPD) (P-interaction < 0.05). We found no evidence of interaction when we tried to replicate previously reported interactions. In addition, no interactions were detected in models with additional covariates.Conclusions: Eight gene-macronutrient interactions were identified for the risk of T2D from the literature. These interactions were not replicated in the EPIC-InterAct study, which mirrored the analyses undertaken in the original reports. Our findings highlight the importance of independent replication of reported interactions.

A preliminary qualitative exploration of dietitians' engagement with genetics and nutritional genomics: perspectives from international leaders.

UNLABELLED: This qualitative study explored the underlying determinants of dietitians' current practice and attitudes about nutritional genomics. METHODS: Sixteen semi-structured interviews were conducted with international leaders selected across each domain of dietetics practice from Australia (n=8) and the United Kingdom (n=8). Interviews explored knowledge, involvement, perceived role, and attitudes about the benefits and barriers of genetics and nutritional genomics. Interviews were transcribed and analysed using thematic analysis. RESULTS: Five key themes were identified: (i) acknowledgment that there are wide applications for nutritional genomics; (ii) a general lack of awareness of nutritional genomics that underlies a knowledge, skills, and confidence gap; (iii) dietitians are patient-orientated and thus are receptive to the public's needs; (iv) the legitimacy of commercialised nutritional genomics products and services; and (v) prioritisation of nutritional genomics amongst other practice-related commitments as well as the influence of the workplace setting. CONCLUSIONS: In order for healthcare services to prepare for the application of nutritional genomics, these social, political, attitudinal, and awareness issues amongst dietitians need to be addressed. Further education in nutritional genomics may help to build awareness, continued research is crucial in determining utility, whilst establishing a healthcare system that supports and rewards this approach may cultivate its adoption.

The application of genetics and nutritional genomics in practice: an international survey of knowledge, involvement and confidence among dietitians in the US, Australia and the UK.

As a result of expanding scientific understanding of the interplay between genetics and dietary risk factors, those involved in nutritional management need to understand genetics and nutritional genomics in order to inform management of individuals and groups. The aim of this study was to measure and determine factors affecting dietitians' knowledge, involvement and confidence in genetics and nutritional genomics across the US, Australia and the UK. A cross-sectional study was undertaken using an online questionnaire that measured knowledge and current involvement and confidence in genetics and nutritional genomics. The questionnaire was distributed to dietitians in the US, Australia and the UK using email lists from the relevant professional associations. Data were collected from 1,844 dietitians who had practiced in the previous 6 months. The main outcomes were knowledge of genetics and nutritional genomics and involvement and confidence in undertaking clinical and educational activities related to genetics and nutritional genomics. Mean scores for knowledge, involvement and confidence were calculated. Analysis of variance and χ (2) analysis were used to compare scores and frequencies. Multivariate linear regression was used to determine predictors of high scores. The results demonstrated significant differences in involvement (p < 0.001) and confidence (p < 0.001) but not knowledge scores (p = 0.119) between countries. Overall, dietitians reported low levels of knowledge (mean knowledge score 56.3 %), involvement (mean number of activities undertaken 20.0-22.7 %) and confidence (mean confidence score 25.8-29.7 %). Significant relationships between confidence, involvement and knowledge were observed. Variables relating to education, experience, sector of employment and attitudes were also significantly associated with knowledge, involvement and confidence. Dietitians' knowledge, involvement and confidence relating to genetics and nutritional genomics remain low and further investigation into factors contributing to this is required.