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) 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.

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.

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 v 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.

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 U.S. SEER-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), 3,073 men died of prostate cancer within 10 years. Among 12 definitions that included TNM stage 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 stage T4 or N1 or M1 or Gleason score ≥8 prostate cancer, as this definition had one of the higher PPVs (0.23, 95% confidence interval [CI] 0.22-0.24) and reasonable sensitivity (0.66, 95% CI 0.64-0.67) 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 stage (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.

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.

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, 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 two 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, IL-6, 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, CRP, quinolinic acid, PAr index, and KTR. The inflammaging signature included ten markers and was strongly associated of mortality (HR per SD=1.40, 95%CI:1.24-1.57, P=2x10 -8), similar to scores based on all age-associated (HR=1.38, 95%CI:1.23-1.55, P=4x10 -8) and mortality-associated markers (HR=1.43, 95%CI:1.28-1.60, P=1x10 -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 ten markers was strongly associated with mortality.

Background The BOADICEA and IBIS breast cancer risk models are used to provide advice on screening intervals and chemoprevention. We evaluated the performance of these models, which both 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 2,783 were from the sub-cohort. 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 expected/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 (0.59-0.64) using BOADICEA. IBIS under-predicted risk (E/O=0.62, 95% CI = 0.48 to 0.80) for women in the lowest risk category (&lt;1.7%) and over-predicted risk (E/O=1.40, 95% CI = 1.18 to 1.67) in the highest risk category (≥5%); Hosmer-Lemeshow test for calibration in quantiles of risk, two-sided P&lt;0.001. BOADICEA under-predicted risk (E/O=0.82, 95% CI = 0.67 to 0.99) in the second highest risk category (3.4%-5%); Hosmer-Lemeshow test, two-sided P=0.02. Conclusions While 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 re-calibration before their clinical and wider use is promoted.

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.