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Abstract Background: Peripheral artery disease (PAD) and coronary artery disease (CAD) represent atherosclerosis in different vascular beds. We used detailed metabolic biomarker profiling to identify common and discordant biomarkers and clarify pathophysiological differences for these vascular diseases. Methods and results: We used 5 prospective cohorts from Finnish population (FINRISK 1997, 2002, 2007, and 2012, and Health 2000; n=31 657; median follow‐up time of 14 years) to estimate associations between >200 metabolic biomarkers and incident PAD and CAD. Metabolic biomarkers were measured with nuclear magnetic resonance, and disease events were obtained from nationwide hospital records. During the follow‐up, 498 incident PAD and 2073 incident CAD events occurred. In age‐ and sex‐adjusted Cox models, apolipoproteins and cholesterol measures were robustly associated with incident CAD (eg, hazard ratio [HR] per SD for higher apolipoprotein B/A‐1 ratio, 1.30; 95% CI, 1.25–1.36), but not with incident PAD (HR per SD for higher apolipoprotein B/A‐1 ratio, 1.04; 95% CI, 0.95–1.14; Pheterogeneity<0.001). In contrast, triglyceride levels in low‐density lipoprotein and high‐density lipoprotein were associated with both end points (Pheterogeneity<0.05). Lower proportion of polyunsaturated fatty acids relative to total fatty acids, and higher concentrations of monounsaturated fatty acids, glycolysis‐related metabolites, and inflammatory protein markers were strongly associated with incident PAD, and many of these associations were stronger for PAD than for CAD (Pheterogeneity<0.001). Most differences in metabolic profiles for PAD and CAD remained when adjusting for traditional risk factors. Conclusions: The metabolic biomarker profile for future PAD risk is distinct from that of CAD. This may represent pathophysiological differences.
Abstract Context: Aging varies between individuals, with profound consequences for chronic diseases and longevity. One hypothesis to explain the diversity is a genetically regulated molecular clock that runs differently between individuals. Large human studies with long enough follow-up to test the hypothesis are rare due to practical challenges, but statistical models of aging are built as proxies for the molecular clock by comparing young and old individuals cross-sectionally. These models remain untested against longitudinal data. Objective: We applied novel methodology to test if cross-sectional modeling can distinguish slow vs accelerated aging in a human population. Methods: We trained a machine learning model to predict age from 153 clinical and cardiometabolic traits. The model was tested against longitudinal data from another cohort. The training data came from cross-sectional surveys of the Finnish population (n = 9708; ages 25–74 years). The validation data included 3 time points across 10 years in the Young Finns Study (YFS; n = 1009; ages 24–49 years). Predicted metabolic age in 2007 was compared against observed aging rate from the 2001 visit to the 2011 visit in the YFS dataset and correlation between predicted vs observed metabolic aging was determined. Results: The cross-sectional proxy failed to predict longitudinal observations (R2 = 0.018%, P = 0.67). Conclusion: The finding is unexpected under the clock hypothesis that would produce a positive correlation between predicted and observed aging. Our results are better explained by a stratified model where aging rates per se are similar in adulthood but differences in starting points explain diverging metabolic fates.
Abstract Objective: Our aim was to develop an automated detection method, for prescreening purposes, of early repolarization (ER) pattern with slur/notch configuration in electrocardiogram (ECG) signals using a waveform prototype-based feature vector for supervised classification. Approach: The feature vectors consist of fragments of the ECG signal where the ER pattern is located, instead of abstract descriptive variables of ECG waveforms. The tested classifiers included linear discriminant analysis, k-nearest neighbor algorithm, and support vector machine (SVM). Main results: SVM showed the best performance in Friedman tests in our test data including 5676 subjects representing 45 408 leads. Accuracies of the different classifiers showed results well over 90%, indicating that the waveform prototype-based feature vector is an effective representation of the differences between ECG signals with and without the ER pattern. The accuracy of inferior ER was 92.74% and 92.21% for lateral ER. The sensitivity achieved was 91.80% and specificity was 92.73%. Significance: The algorithm presented here showed good performance results, indicating that it could be used as a prescreening tool of ER, and it provides an additional identification of critical cases based on the distances to the classifier decision boundary, which are close to the 0.1 mV threshold and are difficult to label.
Abstract Background & aims: Prognostic significance of metabolically healthy overweight and obesity (MHO) is under debate. However the relationship between MHO and health-related quality of life (HRQoL) is less studied. We compared successful aging (longevity plus HRQoL) in men with MHO, metabolically healthy normal weight (MHN) and metabolically unhealthy overweight and obesity (MUO). Methods: In the Helsinki Businessmen Study longitudinal cohort, consisting of men born 1919 to 1934. In 1985/86, overweight (BMI≥25 kg/m2) and metabolic health were determined in 1309 men (median age 60 years). HRQoL was assessed using RAND-36/SF-36 in 2000 and 2007, and all-cause mortality retrieved from registers up to 2018. The proportion of men reaching 90 years was also calculated. Results: Of the men, 469 (35.8%), 538 (41.1%), 276 (21.1%), and 26 (2.0%) were MHN, MHO, MUO and MUN, respectively. During the 32-year follow-up, 72.3% men died. With MHN as reference, adjusted hazard ratio with all-cause mortality was 1.08 (95% confidence interval [CI] 0.93 to 1.27) for MHO, and 1.18 (95% CI 0.95 to 1.47) for MUO. During follow-up, 273 men reached 90 years. With MHN as reference, adjusted odds ratio for MHO was 0.82 (95% CI 0.59 to 1.14) and 0.62 (95% CI 0.41 to 0.95) for MUO. Men in MHN group scored generally highest in RAND-36 HRQoL subscales in 2000 and 2007, of those significantly better in Physical functioning, Role physical, Role emotional, Bodily Pain, and General health sub-scales compared to MHO group in 2000. Conclusions: As compared to MHN, MHO in late midlife does not increase mortality, but impairs odds for successful aging.
Abstract Background/Objectives: This observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools. Subjects/Methods: Firstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys (FINRISK, n = 9708, ages 25–74) and a birth cohort (NFBC1966, n = 3117, age 31 at baseline, age 46 at follow-up) and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study (YFS, n = 1286, ages 24–39 at baseline, 10 years follow-up, three visits) were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression. Results: The four subgroups were characterised at age 39 by high BMI, WHR and dyslipidemia (designated TG-rich); low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the cross-sectional metabolic profile. Conclusions: Age-associated weight variation in adults between 24 and 49 manifests as temporal divergence in BMI and uniform progression of WHR across metabolic health strata.
Abstract Background: Electrocardiographic (ECG) left ventricular hypertrophy (LVH) is an established risk factor for cardiovascular events. However, limited data is available on the prognostic values of different ECG LVH criteria specifically to sudden cardiac death (SCD). Our goal was to assess relationships of different ECG LVH criteria to SCD. Methods: Three traditional and clinically useful (Sokolow–Lyon, Cornell, RaVL) and a recently proposed (Peguero–Lo Presti) ECG LVH voltage criteria were measured in 5730 subjects in the Health 2000 Survey, a national general population cohort study. Relationships between LVH criteria, as well as their selected composites, to SCD were analyzed with Cox regression models. In addition, population-attributable fractions for LVH criteria were calculated. Results: After a mean follow-up of 12.5 ± 2.2 years, 134 SCDs had occurred. When used as continuous variables, all LVH criteria except for RaVL were associated with SCD in multivariable analyses. When single LVH criteria were used as dichotomous variables, only Cornell was significant after adjustments. The dichotomous composite of Sokolow–Lyon and Cornell was also significant after adjustments (hazard ratio for SCD 1.82, 95% confidence interval 1.20–2.70, P = 0.006) and was the only LVH measure that showed statistically significant population-attributable fraction (11.0%, 95% confidence interval 1.9–19.2%, P = 0.019). Conclusions: Sokolow–Lyon, Cornell, and Peguero–Lo Presti ECG, but not RaVL voltage, are associated with SCD risk as continuous ECG voltage LVH variables. When SCD risk assessment/adjustment is performed using a dichotomous ECG LVH measure, composite of Sokolow–Lyon and Cornell voltages is the preferred option.
Abstract Aims: To evaluate risk factors for major adverse cardiac event (MACE) after the first acute coronary syndrome (ACS) and to examine the prevalence of risk factors in post-ACS patients. Methods: We used Finnish population-based myocardial infarction register, FINAMI, data from years 1993–2011 to identify survivors of first ACS (n = 12686), who were then followed up for recurrent events and all-cause mortality for three years. Finnish FINRISK risk factor surveys were used to determine the prevalence of risk factors (smoking, hyperlipidaemia, diabetes and blood pressure) in post-ACS patients (n = 199). Results: Of the first ACS survivors, 48.4% had MACE within three years of their primary event, 17.0% were fatal. Diabetes (p = 4.4 × 10−7), heart failure (HF) during the first ACS attack hospitalization (p = 6.8 × 10−15), higher Charlson index (p = 1.56 × 10−19) and older age (p = .026) were associated with elevated risk for MACE in the three-year follow-up, and revascularization (p = .0036) was associated with reduced risk. Risk factor analyses showed that 23% of ACS survivors continued smoking and cholesterol levels were still high (>5mmol/l) in 24% although 86% of the patients were taking lipid lowering medication. Conclusion: Diabetes, higher Charlson index and HF are the most important risk factors of MACE after the first ACS. Cardiovascular risk factor levels were still high among survivors of first ACS.
Abstract Aims: Angiopoietin-like protein 3 (ANGPTL3) and 4 (ANGPTL4) inhibit lipoprotein lipase (LPL) and represent emerging drug targets to lower circulating triglycerides and reduce cardiovascular risk. To investigate the molecular effects of genetic mimicry of ANGPTL3 and ANGPTL4 inhibition and compare them to the effects of genetic mimicry of LPL enhancement. Methods and results: Associations of genetic variants in ANGPTL3 (rs11207977-T), ANGPTL4 (rs116843064-A), and LPL (rs115849089-A) with an extensive serum lipid and metabolite profile (208 measures) were characterized in six cohorts of up to 61 240 participants. Genetic associations with anthropometric measures, glucose-insulin metabolism, blood pressure, markers of kidney function, and cardiometabolic endpoints via genome-wide summary data were also explored. ANGPTL4 rs116843064-A and LPL rs115849089-A displayed a strikingly similar pattern of associations across the lipoprotein and lipid measures. However, the corresponding associations with ANGPTL3 rs11207977-T differed, including those for low-density lipoprotein and high-density lipoprotein particle concentrations and compositions. All three genotypes associated with lower concentrations of an inflammatory biomarker glycoprotein acetyls and genetic mimicry of ANGPTL3 inhibition and LPL enhancement were also associated with lower C-reactive protein. Genetic mimicry of ANGPTL4 inhibition and LPL enhancement were associated with a lower waist-to-hip ratio, improved insulin-glucose metabolism, and lower risk of coronary heart disease and type 2 diabetes, whilst genetic mimicry of ANGPTL3 was associated with improved kidney function. Conclusions: Genetic mimicry of ANGPTL4 inhibition and LPL enhancement have very similar systemic metabolic effects, whereas genetic mimicry of ANGPTL3 inhibition showed differing metabolic effects, suggesting potential involvement of pathways independent of LPL. Genetic mimicry of ANGPTL4 inhibition and LPL enhancement were associated with a lower risk of coronary heart disease and type 2 diabetes. These findings reinforce evidence that enhancing LPL activity (either directly or via upstream effects) through pharmacological approaches is likely to yield benefits to human health.
Abstract The role of metabolic syndrome (MetS) as a preceding metabolic state for type 2 diabetes and cardiovascular disease is widely recognised. To accumulate knowledge of the pathological mechanisms behind the condition at the methylation level, we conducted an epigenome-wide association study (EWAS) of MetS and its components, testing 1187 individuals of European ancestry for approximately 470 000 methylation sites throughout the genome. Methylation site cg19693031 in gene TXNIP —previously associated with type 2 diabetes, glucose and lipid metabolism, associated with fasting glucose level (P = 1.80 × 10−8). Cg06500161 in gene ABCG1 associated both with serum triglycerides (P = 5.36 × 10−9) and waist circumference (P = 5.21 × 10−9). The previously identified type 2 diabetes–associated locus cg08309687 in chromosome 21 associated with waist circumference for the first time (P = 2.24 × 10−7). Furthermore, a novel HDL association with cg17901584 in chromosome 1 was identified (P = 7.81 × 10−8). Our study supports previous genetic studies of MetS, finding that lipid metabolism plays a key role in pathology of the syndrome. We provide evidence regarding a close interplay with glucose metabolism. Finally, we suggest that in attempts to identify methylation loci linking separate MetS components, cg19693031 appears to represent a strong candidate.
Abstract Background: GlycA is a nuclear magnetic resonance (NMR) spectroscopy biomarker that predicts risk of disease from myriad causes. It is heterogeneous; arising from five circulating glycoproteins with dynamic concentrations: alpha-1 antitrypsin (AAT), alpha-1-acid glycoprotein (AGP), haptoglobin (HP), transferrin (TF), and alpha-1-antichymotrypsin (AACT). The contributions of each glycoprotein to the disease and mortality risks predicted by GlycA remain unknown. Methods: We trained imputation models for AAT, AGP, HP, and TF from NMR metabolite measurements in 626 adults from a population cohort with matched NMR and immunoassay data. Levels of AAT, AGP, and HP were estimated in 11,861 adults from two population cohorts with eight years of follow-up, then each biomarker was tested for association with all common endpoints. Whole blood gene expression data was used to identify cellular processes associated with elevated AAT. Results: Accurate imputation models were obtained for AAT, AGP, and HP but not for TF. While AGP had the strongest correlation with GlycA, our analysis revealed variation in imputed AAT levels was the most predictive of morbidity and mortality for the widest range of diseases over the eight year follow-up period, including heart failure (meta-analysis hazard ratio = 1.60 per standard deviation increase of AAT, P-value = 1×10−10), influenza and pneumonia (HR = 1.37, P = 6×10−10), and liver diseases (HR = 1.81, P = 1×10−6). Transcriptional analyses revealed association of elevated AAT with diverse inflammatory immune pathways. Conclusions: This study clarifies the molecular underpinnings of the GlycA biomarker’s associated disease risk, and indicates a previously unrecognised association between elevated AAT and severe disease onset and mortality.
Abstract Background and aims: Apolipoprotein A-I (apoA-I) infusions represent a potential novel therapeutic approach for the prevention of coronary artery disease (CAD). Although circulating apoA-I concentrations inversely associate with risk of CAD, the evidence base of this representing a causal relationship is lacking. The aim was to assess the causal role of apoA-I using human genetics. Methods: We identified a variant (rs12225230) in APOA1 locus that associated with circulating apoA-I concentrations (p < 5 × 10−8) in 20,370 Finnish participants, and meta-analyzed our data with a previous GWAS of apoA-I. We obtained genetic estimates of CAD from UK Biobank and CARDIoGRAMplusC4D (totaling 122,733 CAD cases) and conducted a two-sample Mendelian randomization analysis. We compared our genetic findings to observational associations of apoA-I with risk of CAD in 918 incident CAD cases among 11,535 individuals from population-based prospective cohorts. Results: ApoA-I was associated with a lower risk of CAD in observational analyses (HR 0.81; 95%CI: 0.75, 0.88; per 1-SD higher apoA-I), with the association showing a dose-response relationship. Rs12225230 associated with apoA-I concentrations (per-C allele beta 0.076 SD; SE: 0.013; p = 1.5 × 10−9) but not with confounders. In Mendelian randomization analyses, apoA-I was not related to risk of CAD (OR 1.13; 95%CI: 0.98,1.30 per 1-SD higher apoA-I), which was different from the observational association. Similar findings were observed using an independent ABCA1 variant in sensitivity analysis. Conclusions: Genetic evidence fails to support a cardioprotective role for apoA-I. This is in line with the cumulative evidence showing that HDL-related phenotypes are unlikely to have a protective role in CAD.
Abstract Background: Observational findings for high-density lipoprotein (HDL)-mediated cholesterol efflux capacity (HDL-CEC) and coronary heart disease (CHD) appear inconsistent, and knowledge of the genetic architecture of HDL-CEC is limited. Objectives: A large-scale observational study on the associations of HDL-CEC and other HDL-related measures with CHD and the largest genome-wide association study (GWAS) of HDL-CEC. Participants/Methods: Six independent cohorts were included with follow-up data for 14,438 participants to investigate the associations of HDL-related measures with incident CHD (1,570 events). The GWAS of HDL-CEC was carried out in 20,372 participants. Results: HDL-CEC did not associate with CHD when adjusted for traditional risk factors and HDL cholesterol (HDL-C). In contradiction, almost all HDL-related concentration measures associated consistently with CHD after corresponding adjustments. There were no genetic loci associated with HDL-CEC independent of HDL-C and triglycerides. Conclusions: HDL-CEC is not unequivocally associated with CHD in contrast to HDL-C, apolipoprotein A-I, and most of the HDL subclass particle concentrations.
Abstract Objective: Previous studies on the association between metabolic biomarkers and hypertension have been limited by small sample sizes, low number of studied biomarkers, and cross-sectional study design. In the largest study to date, we assess the cross-sectional and longitudinal associations between high-abundance serum biomarkers and blood pressure (BP). Methods: We studied cross-sectional (N = 36 985; age 50.5 ± 14.2; 53.1% women) and longitudinal (N = 4197; age 49.4 ± 11.8, 55.3% women) population samples of Finnish individuals. We included 53 serum biomarkers and other detailed lipoprotein subclass measures in our analyses. We studied the associations between serum biomarkers and BP using both conventional statistical methods and a machine learning algorithm (gradient boosting) while adjusting for clinical risk factors. Results: Fifty-one of 53 serum biomarkers were cross-sectionally related to BP (adjusted P < 0.05 for all). Conventional linear regression modeling demonstrated that LDL cholesterol, remnant cholesterol, apolipoprotein B, and acetate were positively, and HDL particle size was negatively, associated with SBP change over time (adjusted P < 0.05 for all). Adding serum biomarkers (cross-sectional root-mean-square error: 16.27 mmHg; longitudinal: 17.61 mmHg) in the model with clinical measures (cross-sectional: 16.70 mmHg; longitudinal 18.52 mmHg) improved the machine learning model fit. Glucose, albumin, triglycerides in LDL, glycerol, VLDL particle size, and acetoacetate had the highest importance scores in models related to current or future BP. Conclusions: Our results suggest that serum lipids, and particularly LDL-derived and VLDL-derived cholesterol measures, and glucose metabolism abnormalities are associated with hypertension onset. Use of serum metabolite determination could improve identification of individuals at high risk of developing hypertension.
Abstract Aims/hypothesis: Metabolomics technologies have identified numerous blood biomarkers for type 2 diabetes risk in case−control studies of middle-aged and older individuals. We aimed to validate existing and identify novel metabolic biomarkers predictive of future diabetes in large cohorts of young adults. Methods: NMR metabolomics was used to quantify 229 circulating metabolic measures in 11,896 individuals from four Finnish observational cohorts (baseline age 24–45 years). Associations between baseline metabolites and risk of developing diabetes during 8–15 years of follow-up (392 incident cases) were adjusted for sex, age, BMI and fasting glucose. Prospective metabolite associations were also tested with fasting glucose, 2 h glucose and HOMA-IR at follow-up. Results: Out of 229 metabolic measures, 113 were associated with incident type 2 diabetes in meta-analysis of the four cohorts (ORs per 1 SD: 0.59–1.50; p< 0.0009). Among the strongest biomarkers of diabetes risk were branched-chain and aromatic amino acids (OR 1.31–1.33) and triacylglycerol within VLDL particles (OR 1.33–1.50), as well as linoleic n-6 fatty acid (OR 0.75) and non-esterified cholesterol in large HDL particles (OR 0.59). The metabolic biomarkers were more strongly associated with deterioration in post-load glucose and insulin resistance than with future fasting hyperglycaemia. A multi-metabolite score comprised of phenylalanine, non-esterified cholesterol in large HDL and the ratio of cholesteryl ester to total lipid in large VLDL was associated with future diabetes risk (OR 10.1 comparing individuals in upper vs lower fifth of the multi-metabolite score) in one of the cohorts (mean age 31 years). Conclusions/interpretation: Metabolic biomarkers across multiple molecular pathways are already predictive of the long-term risk of diabetes in young adults. Comprehensive metabolic profiling may help to target preventive interventions for young asymptomatic individuals at increased risk.
Abstract Background: Cardiomyocytes secrete atrial natriuretic peptide (ANP) and B-type natriuretic peptide (BNP) in response to mechanical stretching, making them useful clinical biomarkers of cardiac stress. Both human and animal studies indicate a role for ANP as a regulator of blood pressure with conflicting results for BNP. Methods and Results: We used genome-wide association analysis (n=6296) to study the effects of genetic variants on circulating natriuretic peptide concentrations and compared the impact of natriuretic peptide–associated genetic variants on blood pressure (n=27 059). Eight independent genetic variants in 2 known (NPPA-NPPB and POC1B-GALNT4) and 1 novel locus (PPP3CC) associated with midregional proANP (MR-proANP), BNP, aminoterminal proBNP (NT-proBNP), or BNP:NT-proBNP ratio. The NPPA-NPPB locus containing the adjacent genes encoding ANP and BNP harbored 4 independent cis variants with effects specific to either midregional proANP or BNP and a rare missense single nucleotide polymorphism in NT-proBNP seriously altering its measurement. Variants near the calcineurin catalytic subunit gamma gene PPP3CC and the polypeptide N-acetylgalactosaminyltransferase 4 gene GALNT4 associated with BNP:NT-proBNP ratio but not with BNP or midregional proANP, suggesting effects on the post-translational regulation of proBNP. Out of the 8 individual variants, only those correlated with midregional proANP had a statistically significant albeit weak impact on blood pressure. The combined effect of these 3 single nucleotide polymorphisms also associated with hypertension risk (P=8.2×10−4). Conclusions: Common genetic differences affecting the circulating concentration of ANP associated with blood pressure, whereas those affecting BNP did not, highlighting the blood pressure–lowering effect of ANP in the general population.
Abstract Aims/hypothesis: A validated mass-spectrometric method was applied to measure Cer(d18:1/16:0), Cer(d18:1/18:0), Cer(d18:1/24:0) and Cer(d18:1/24:1) from serum or plasma samples. These ceramides were analysed in a population-based risk factor study (FINRISK 2002, n = 8045), in a cohort of participants undergoing elective coronary angiography for suspected stable angina pectoris (Western Norway Coronary Angiography Cohort [WECAC], n = 3344) and in an intervention trial investigating improved methods of lifestyle modification for individuals at high risk of the metabolic syndrome (Prevent Metabolic Syndrome [PrevMetSyn], n = 371). Diabetes risk score models were developed to estimate the 10 year risk of incident diabetes. Methods: A validated mass-spectrometric method was applied to measure Cer(d18:1/16:0), Cer(d18:1/18:0), Cer(d18:1/24:0) and Cer(d18:1/24:1) from serum or plasma samples. These ceramides were analysed in a population-based risk factor study (FINRISK 2002, n = 8045), in a cohort of participants undergoing elective coronary angiography for suspected stable angina pectoris (Western Norway Coronary Angiography Cohort [WECAC], n = 3344) and in an intervention trial investigating improved methods of lifestyle modification for individuals at high risk of the metabolic syndrome (Prevent Metabolic Syndrome [PrevMetSyn], n = 371). Diabetes risk score models were developed to estimate the 10 year risk of incident diabetes. Results: Analysis in FINRISK 2002 showed that the Cer(d18:1/18:0)/Cer(d18:1/16:0) ceramide ratio was predictive of incident diabetes (HR per SD 2.23, 95% CI 2.05, 2.42), and remained significant after adjustment for several risk factors, including BMI, fasting glucose and HbA1c (HR 1.34, 95% CI 1.14, 1.57). The finding was validated in the WECAC study (unadjusted HR 1.81, 95% CI 1.53, 2.14; adjusted HR 1.39, 95% CI 1.16, 1.66). In the intervention trial, the ceramide ratio and diabetes risk scores significantly decreased in individuals who had 5% or more weight loss. Conclusions/interpretation: The Cer(d18:1/18:0)/Cer(d18:1/16:0) ratio is an independent predictive biomarker for incident diabetes, and may be modulated by lifestyle intervention.