👤 Asier Galan

The timing of physical activity may influence metabolic health through interactions with circadian rhythms, yet its role in type 2 diabetes mellitus (T2DM) development is unclear. We investigated associations between time-of-day-specific physical activity and incident T2DM, and whether theoretically reallocating activity from morning to later in the day was associated with changes in T2DM risk. We included 4615 participants from The Maastricht Study cohort without diabetes (age 59.2 ± 8.6 years; 56.3% women). Device-based physical activity was measured over 7 days using activPAL monitors and classified into light-intensity physical activity (LPA) and moderate-to-vigorous intensity physical activity (MVPA), for morning (06:00-11:59 AM), afternoon (12:00-17:59 PM), evening (18:00-23:59 PM) and night (00:00-05:59 AM). Incident T2DM was assessed during a median 8.2-year follow-up. Cox proportional hazard and isotemporal substitution models were used, adjusted for sociodemographic and lifestyle factors, including diet, employment and sleep duration. During follow-up, 168 participants (3.6%) developed T2DM. Each additional 10 min/day of afternoon LPA or MVPA was associated with lower T2DM risk (LPA: hazard ratio [HR] 0.82, 95% confidence interval [CI] 0.70-0.97; MVPA: HR 0.85, 95% CI 0.72-1.00). Evening MVPA was also inversely associated with T2DM risk (0.65; 0.45-0.93), whereas night-time MVPA was associated with an increased risk (3.64; 1.30-10.17). No significant associations were found of morning LPA and MVPA or evening and night LPA with T2DM incidence. Substitution analyses indicated that reallocating 10 min of morning LPA to afternoon LPA (HR 0.71; 0.54-0.95) or morning MVPA to evening MVPA (HR: 0.64; 0.43-0.96) was associated with a lower T2DM risk, while no other significant associations were observed. Later-day physical activity, particularly in the afternoon, was associated with a lower incidence of T2DM, independent of intensity. This highlights the potential relevance of activity timing in relation to T2DM incidence. Show less

Impaired glucose tolerance (IGM) and type 2 diabetes mellitus (T2DM) are associated with less optimal time spent in 24-hour movement behaviors (24 h-MBs) compared to people with normoglycemia (NG). We aimed to investigate how 24 h-MBs change over time and whether changes in 24 h-MBs differ between adults according to glycemic trajectories over time. Participants ( The online version contains supplementary material available at 10.1038/s41598-025-33099-z. Show less

Idiopathic dilated cardiomyopathy (iDCM) is a primary myocardial disorder with an unknown aetiology, characterized by reduced contractility and ventricular dilation of the left or both ventricles. Naturally occurring canine iDCM was used herein to identify serum proteomic signature of the disease compared to the healthy state, providing an insight into underlying mechanisms and revealing proteins with biomarker potential. To achieve this, we used high-throughput label-based quantitative LC-MS/MS proteomics approach and bioinformatics analysis of the in silico inferred interactome protein network created from the initial list of differential proteins. To complement the proteomic analysis, serum biochemical parameters and levels of know biomarkers of cardiac function were measured. Several proteins with biomarker potential were identified, such as inter-alpha-trypsin inhibitor heavy chain H4, microfibril-associated glycoprotein 4 and apolipoprotein A-IV, which were validated using an independent method (Western blotting) and showed high specificity and sensitivity according to the receiver operating characteristic curve analysis. Bioinformatics analysis revealed involvement of different pathways in iDCM, such as complement cascade activation, lipoprotein particles dynamics, elastic fibre formation, GPCR signalling and respiratory electron transport chain. Idiopathic dilated cardiomyopathy is a severe primary myocardial disease of unknown cause, affecting both humans and dogs. This study is a contribution to the canine heart disease research by means of proteomic and bioinformatic state of the art analyses, following similar approach in human iDCM research. Importantly, we used serum as non-invasive and easily accessible biological source of information and contributed to the scarce data on biofluid proteome research on this topic. Bioinformatics analysis revealed biological pathways modulated in canine iDCM with potential of further targeted research. Also, several proteins with biomarker potential have been identified and successfully validated. Show less

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes. Show less

Blood low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglyceride levels are risk factors for cardiovascular disease. To dissect the polygenic basis of these traits, we conducted genome-wide association screens in 19,840 individuals and replication in up to 20,623 individuals. We identified 30 distinct loci associated with lipoprotein concentrations (each with P < 5 x 10(-8)), including 11 loci that reached genome-wide significance for the first time. The 11 newly defined loci include common variants associated with LDL cholesterol near ABCG8, MAFB, HNF1A and TIMD4; with HDL cholesterol near ANGPTL4, FADS1-FADS2-FADS3, HNF4A, LCAT, PLTP and TTC39B; and with triglycerides near AMAC1L2, FADS1-FADS2-FADS3 and PLTP. The proportion of individuals exceeding clinical cut points for high LDL cholesterol, low HDL cholesterol and high triglycerides varied according to an allelic dosage score (P < 10(-15) for each trend). These results suggest that the cumulative effect of multiple common variants contributes to polygenic dyslipidemia. Show less

To identify genetic variants influencing plasma lipid concentrations, we first used genotype imputation and meta-analysis to combine three genome-wide scans totaling 8,816 individuals and comprising 6,068 individuals specific to our study (1,874 individuals from the FUSION study of type 2 diabetes and 4,184 individuals from the SardiNIA study of aging-associated variables) and 2,758 individuals from the Diabetes Genetics Initiative, reported in a companion study in this issue. We subsequently examined promising signals in 11,569 additional individuals. Overall, we identify strongly associated variants in eleven loci previously implicated in lipid metabolism (ABCA1, the APOA5-APOA4-APOC3-APOA1 and APOE-APOC clusters, APOB, CETP, GCKR, LDLR, LPL, LIPC, LIPG and PCSK9) and also in several newly identified loci (near MVK-MMAB and GALNT2, with variants primarily associated with high-density lipoprotein (HDL) cholesterol; near SORT1, with variants primarily associated with low-density lipoprotein (LDL) cholesterol; near TRIB1, MLXIPL and ANGPTL3, with variants primarily associated with triglycerides; and a locus encompassing several genes near NCAN, with variants strongly associated with both triglycerides and LDL cholesterol). Notably, the 11 independent variants associated with increased LDL cholesterol concentrations in our study also showed increased frequency in a sample of coronary artery disease cases versus controls. Show less