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Background De novo lipogenesis (DNL) is the primary metabolic pathway synthesizing fatty acids from carbohydrates, protein, or alcohol. Our aim was to examine associations of in vivo levels of selected fatty acids (16:0, 16:1n7, 18:0, 18:1n9) in DNL with incidence of type 2 diabetes (T2D). Methods and findings Seventeen cohorts from 12 countries (7 from Europe, 7 from the United States, 1 from Australia, 1 from Taiwan; baseline years = 1970–1973 to 2006–2010) conducted harmonized individual-level analyses of associations of DNL-related fatty acids with incident T2D. In total, we evaluated 65,225 participants (mean ages = 52.3–75.5 years; % women = 20.4%–62.3% in 12 cohorts recruiting both sexes) and 15,383 incident cases of T2D over the 9-year follow-up on average. Cohort-specific association of each of 16:0, 16:1n7, 18:0, and 18:1n9 with incident T2D was estimated, adjusted for demographic factors, socioeconomic characteristics, alcohol, smoking, physical activity, dyslipidemia, hypertension, menopausal status, and adiposity. Cohort-specific associations were meta-analyzed with an inverse-variance-weighted approach. Each of the 4 fatty acids positively related to incident T2D. Relative risks (RRs) per cohort-specific range between midpoints of the top and bottom quintiles of fatty acid concentrations were 1.53 (1.41–1.66; p < 0.001) for 16:0, 1.40 (1.33–1.48; p < 0.001) for 16:1n-7, 1.14 (1.05–1.22; p = 0.001) for 18:0, and 1.16 (1.07–1.25; p < 0.001) for 18:1n9. Heterogeneity was seen across cohorts (I2 = 51.1%–73.1% for each fatty acid) but not explained by lipid fractions and global geographical regions. Further adjusted for triglycerides (and 16:0 when appropriate) to evaluate associations independent of overall DNL, the associations remained significant for 16:0, 16:1n7, and 18:0 but were attenuated for 18:1n9 (RR = 1.03, 95% confidence interval (CI) = 0.94–1.13). These findings had limitations in potential reverse causation and residual confounding by imprecisely measured or unmeasured factors. Conclusions Concentrations of fatty acids in the DNL were positively associated with T2D incidence. Our findings support further work to investigate a possible role of DNL and individual fatty acids in the development of T2D., Fumiaki Imamura and colleagues investigate the association between concentrations of fatty acid involved in de-novo lipogensis and incidence of Type 2 diabetes., Author summary Why was this study done? De novo lipogenesis (DNL) is a metabolic pathway involved in the endogenous synthesis of specific fatty acids, such as 16:0, 16:1n7, 18:0, and 18:1n9, and it is linked to the pathophysiology of cardiometabolic diseases, including type 2 diabetes (T2D). Circulating or tissue concentrations of these fatty acids have been investigated for the associations with T2D incidence in epidemiological research. However, published studies reported inconsistent associations inconsistently and were subject to publication bias. Summary evidence is not available to date for the associations between these fatty acids and T2D incidence. An integration of available cohort studies would increase statistical power and allow assessment of generalizability, standardization of analytical strategies, and evidence synthesis with the potential publication bias minimized. What did the researchers do and find? As a part of the Fatty Acids and Outcomes Research Consortium (FORCE), we conducted new individual-participant data analyses of 17 cohort studies of a total of 65,225 adults free of T2D at baseline, among whom 15,383 developed incident T2D over up to 20 years of follow-up. The cohort studies analyzed the associations between fatty acids (16:0, 16:1n7, 18:0, and 18:1n9) and the risk of developing T2D with standardized analytic strategy. In pooled analyses, each of the fatty acids was positively associated with a higher risk of developing T2D. The associations were independent of major risk factors for T2D, such as age, sex, race/ethnicity, socioeconomic characteristics, smoking status, physical activity, and obesity. What do these findings mean? The findings provide the first summary evidence to date for the positive relationships of concentrations of the DNL-related fatty acids with a risk of T2D, indicating the strong relevance of DNL and its determinants to the development of T2D. These fatty acids potentially reflect the status of DNL activity, which may be stimulated or suppressed by a combination of carbohydrate intake, alcohol intake, polyunsaturated fatty acid intake, and other lifestyle and clinical factors. Therefore, the current findings indicate the need for investigation into determinants and consequences of elevated concentrations of these fatty acids. Despite several advantages of our individual-level data analysis in this pooling project, the results cannot establish whether elevated concentrations of these fatty acids caused the development of T2D or whether underlying peripheral or hepatic insulin resistance, for example, may elevate both the fatty acid concentrations and the risk of T2D independently.

47 p.-11 fig., Mitochondrial DNA (mtDNA) variants influence the risk of late-onset human diseases, but the reasons for this are poorly understood. Undertaking a hypothesis-free analysis of 5,689 blood-derived biomarkers with mtDNA variants in 16,220 healthy donors, here we show that variants defining mtDNA haplogroups Uk and H4 modulate the level of circulating N-formylmethionine (fMet), which initiates mitochondrial protein translation. In human cytoplasmic hybrid (cybrid) lines, fMet modulated both mitochondrial and cytosolic proteins on multiple levels, through transcription, post-translational modification and proteolysis by an N-degron pathway, abolishing known differences between mtDNA haplogroups. In a further 11,966 individuals, fMet levels contributed to all-cause mortality and the disease risk of several common cardiovascular disorders. Together, these findings indicate that fMet plays a key role in common age-related disease through pleiotropic effects on cell proteostasis., This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division (Welsh Government), the Public Health Agency (Northern Ireland), the British Heart Foundation and Wellcome. N.C. is supported by an EBI–Sanger Postdoctoral Fellowship. A.G.-D. receives funding from the NIHR Biomedical Research Centre based at Cambridge University Hospitals NHS Foundation Trust. E.Y.-D. was funded by the Isaac Newton Trust/Wellcome Trust ISSF/University of Cambridge Joint Research Grants Scheme. J.M.M.H. was funded by the NIHR Cambridge BRC (BRC-1215-20014)*. P.F.C.is a Wellcome Trust Principal Research Fellow (212219/Z/18/Z) and a UK NIHR Senior Investigator, who receives support from the Medical Research Council Mitochondrial Biology Unit (MC_UU_00015/9), the Evelyn Trust and the NIHR Biomedical Research Centre based at Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge. C.H.W.-G. is supported by a RCUK/UKRI Research Innovation Fellowship awarded by the Medical Research Council (MR/R007446/1) and by the Cambridge Centre for Parkinson-Plus. N.S. is supported by the Wellcome Trust(grant number 098051 until 30 September 2016 and 206194 from 1 October 2016),the Cambridge BHF Centre of Research Excellence (RE/18/1/34212) and the NIHR Cambridge Biomedical Research Centre Biomedical Resources Grant, University of Cambridge, Cardiovascular Theme (RG64226). J.D. holds a British Heart Foundation Professorship and an NIHR Senior Investigator Award*. R.A.L. is supported by grants from Parkinson’s UK. Sequencing of INTERVAL samples was supported by the Wellcome Trust (grant number 206194). Metabolon metabolomics assays were funded by the NIHR BioResource, the Wellcome Trust (grant number 206194) and the NIHR Cambridge BRC (BRC-1215-20014)*. Nightingale Health NMR assays were funded by the European Commission Framework Programme 7 (HEALTH-F2-2012-279233). SomaLogic assays were funded by Merck and the NIHR Cambridge BRC (BRC-1215-20014)*. ICICLE-PD was funded by Parkinson’s UK (J-0802, G-1301, G-1507) and the Lockhart Parkinson’s Disease Research Fund and supported by the NIHR Newcastle Biomedical Research Unit and the NIHR Cambridge Biomedical Research Centre (146281). The EPIC-Norfolk study (https://doi.org/10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1 and MC-UU_12015/1) and Cancer Research UK (C864/A14136). Genetic work in the EPIC-Norfolk study was funded by the Medical Research Council (MC_PC_13048). Metabolite measurements in the EPIC-Norfolk study were supported by the MRC Cambridge Initiative in Metabolic Science (MR/L00002/1) and the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372.

Background We aimed to investigate prospective associations of circulating or adipose tissue odd-chain fatty acids 15:0 and 17:0 and trans-palmitoleic acid, t16:1n-7, as potential biomarkers of dairy fat intake, with incident type 2 diabetes (T2D). Methods and findings Sixteen prospective cohorts from 12 countries (7 from the United States, 7 from Europe, 1 from Australia, 1 from Taiwan) performed new harmonised individual-level analysis for the prospective associations according to a standardised plan. In total, 63,682 participants with a broad range of baseline ages and BMIs and 15,180 incident cases of T2D over the average of 9 years of follow-up were evaluated. Study-specific results were pooled using inverse-variance–weighted meta-analysis. Prespecified interactions by age, sex, BMI, and race/ethnicity were explored in each cohort and were meta-analysed. Potential heterogeneity by cohort-specific characteristics (regions, lipid compartments used for fatty acid assays) was assessed with metaregression. After adjustment for potential confounders, including measures of adiposity (BMI, waist circumference) and lipogenesis (levels of palmitate, triglycerides), higher levels of 15:0, 17:0, and t16:1n-7 were associated with lower incidence of T2D. In the most adjusted model, the hazard ratio (95% CI) for incident T2D per cohort-specific 10th to 90th percentile range of 15:0 was 0.80 (0.73–0.87); of 17:0, 0.65 (0.59–0.72); of t16:1n7, 0.82 (0.70–0.96); and of their sum, 0.71 (0.63–0.79). In exploratory analyses, similar associations for 15:0, 17:0, and the sum of all three fatty acids were present in both genders but stronger in women than in men (pinteraction < 0.001). Whereas studying associations with biomarkers has several advantages, as limitations, the biomarkers do not distinguish between different food sources of dairy fat (e.g., cheese, yogurt, milk), and residual confounding by unmeasured or imprecisely measured confounders may exist. Conclusions In a large meta-analysis that pooled the findings from 16 prospective cohort studies, higher levels of 15:0, 17:0, and t16:1n-7 were associated with a lower risk of T2D., Fumiaki Imamura and colleagues reveal a correlation between higher levels of circulating and adipose odd-chain fatty and trans-palmitoleic acids and lower risk of developing diabetes., Author summary Why was this study done? Effects of dairy fat on type 2 diabetes (T2D) are not well established. While dairy fat contains palmitic acid that could increase risk of T2D, it also contains several other types of fatty acids and further reflects specific foods, such as cheese or yogurt, that could reduce risk. Most prior studies of dairy foods and T2D have relied on self-reported dietary questionnaires, which may have errors or bias in memory as well as challenges in assessing less apparent sources of dairy fat such as in creams, sauces, cheeses, and cooking fats in mixed meals and prepared foods. Circulating and tissue biomarker concentrations of odd-chain saturated fats (15:0, 17:0) and natural ruminant trans-fats (trans-16:1n7) at least partly reflect dairy fat consumption, help capture multiple dietary sources without relying on memory or subjective reporting, and reflect a complementary approach to investigate associations with T2D. A consortium strategy combining all available studies maximises statistical power and generalizability, allows standardised analytical approaches and methods including of key population subgroups, and minimises potential for publication bias. What did the researchers do and find? We conducted a consortium project to pool new participant-level analyses of 16 cohort studies as part of the Fatty Acids and Outcomes Research Consortium (FORCE), including a total of 63,682 adults free of T2D at baseline, among whom 15,158 developed incident T2D over up to 20 years of follow-up. Participating studies conducted standardised analysis of the prospective associations between fatty acid biomarkers (15:0, 17:0, trans-16:1n7, and their sum) and the risk of developing T2D. Pooling all studies, each of the biomarkers and their sum were associated with lower risk of developing T2D, independently of major risk factors for T2D, including age, sex, race/ethnicity, socioeconomic status, physical activity, and obesity. For example, for the sum of these biomarkers, participants with higher levels experienced 29% (95% CI 21% to 37%) lower risk of T2D than adults with lower levels, comparing between the midpoints of the top fifth and the bottom fifth of concentrations. What do these findings mean? Higher circulating and tissue concentrations of odd-chain saturated fats and a natural ruminant trans-fat are associated with lower risk of T2D. While these biomarkers are known to reflect dairy fat consumption, their levels could also be influenced by other unknown factors. The findings support the need for investigation of determinants of levels of these fatty acids as well as health effects of dairy fat in interventional studies. Despite the several advantages of evaluating fatty acid biomarkers, the results cannot distinguish between different types of dairy foods (e.g., milk, cheese, yogurt, others), which could have differential effects. The findings provide the strongest evidence to date for relationships of these fatty acid biomarkers with T2D, informing the potential health effects and corresponding dietary recommendations for consumption of selected dairy products.