Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication ...studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self-administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age(2), sex, study center (for multicenter studies), and the marginal terms for physical activity and the GRS. These results were combined using meta-analysis weighted by cohort sample size. The meta-analysis yielded a statistically significant GRS × physical activity interaction effect estimate (Pinteraction = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts (n = 39,810, Pinteraction = 0.014 vs. n = 71,611, Pinteraction = 0.275 for Europeans). In secondary analyses, both the FTO rs1121980 (Pinteraction = 0.003) and the SEC16B rs10913469 (Pinteraction = 0.025) variants showed evidence of SNP × physical activity interactions. This meta-analysis of 111,421 individuals provides further support for an interaction between physical activity and a GRS in obesity disposition, although these findings hinge on the inclusion of cohorts from North America, indicating that these results are either population-specific or non-causal.
Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as ...this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning.
We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one.
In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.
ClinicalTrials.gov NCT03814915.
Type III hyperlipidaemia (T3HL) is characterised by equimolar increases in plasma triglycerides (TG) and cholesterol in <10% of APOE22 carriers conveying high cardiovascular disease (CVD) risk. We ...investigate the role of a weighted triglyceride-raising polygenic score (TG.PS) precipitating T3HL.
The TG.PS (restricted to genome-wide significance and weighted by published independent effect estimates) was applied to the Oxford Biobank (OBB, n = 6952) and the UK Biobank (UKB, n = 460,037), to analyse effects on plasma lipid phenotypes. Fasting plasma lipid, lipoprotein biochemistry and NMR lipoprotein profiles were analysed in OBB. CVD prevalence/incidence was examined in UKB.
One TG.PS standard-deviation (SD) was associated with 13.0% (95% confidence-interval 12.0–14.0%) greater TG in OBB and 15.2% (15.0–15.4%) in UKB. APOE22 carriers had 19.0% (1.0–39.0%) greater TG in UKB. Males were more susceptible to TG.PS effects (4.0% (2.0–6.0%) greater TG with 1 TG.PS SD in OBB, 1.6% (1.3–1.9%) in UKB) than females. There was no interaction between APOE22 and TG.PS, BMI, sex or age on TG. APOE22 carriers had lower apolipoprotein B (apoB) (OBB; −0.35 (−0.29 to −0.40)g/L, UKB; −0.41 (−0.405 to −0.42)g/L). NMR lipoprotein lipid concentrations were discordant to conventional biochemistry in APOE22 carriers.
In APOE22 compared with APOE33, CVD was no more prevalent in similarly hypertriglyceridaemic participants (OR 0.97 95%CI 0.76–1.25), but was less prevalent in normolipidaemia (OR 0.81, 95%CI 0.69–0.95); no differences were observed in CVD incidence.
TG.PS confers an additive risk for developing T3HL, that is of comparable effect size to conventional risk factors. The protective effect of APOE22 for prevalent CVD is consistent with lower apoB in APOE22 carriers.
•APOE22 confers a protective effect against cardiovascular disease in normolipidaemia, which is absent in hyperlipidaemia.•A triglyceride-raising polygenic score is an additive T3HL risk factor in APOE22 carriers comparable to classic risk factors.•Considering polygenic lipid risk in APOE22 may improve prevention, diagnosis and disease management for T3HL.
Subclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on ...their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D.
The study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders.
At baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1-3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18-1.92) for subgroup 2 and 1.88 (-0.08-3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose.
Different glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk.
Aims/hypothesis
The aim of this study was to identify differentially expressed long non-coding RNAs (lncRNAs) and mRNAs in whole blood of people with type 2 diabetes across five different clusters: ...severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild diabetes (MD) and mild diabetes with high HDL-cholesterol (MDH)
.
This was to increase our understanding of different molecular mechanisms underlying the five putative clusters of type 2 diabetes.
Methods
Participants in the Hoorn Diabetes Care System (DCS) cohort were clustered based on age, BMI, HbA
1c
, C-peptide and HDL-cholesterol
.
Whole blood RNA-seq was used to identify differentially expressed lncRNAs and mRNAs in a cluster compared with all others. Differentially expressed genes were validated in the Innovative Medicines Initiative DIabetes REsearCh on patient straTification (IMI DIRECT) study. Expression quantitative trait loci (eQTLs) for differentially expressed RNAs were obtained from a publicly available dataset
.
To estimate the causal effects of RNAs on traits, a two-sample Mendelian randomisation analysis was performed using public genome-wide association study (GWAS) data.
Results
Eleven lncRNAs and 175 mRNAs were differentially expressed in the MOD cluster, the lncRNA
AL354696.2
was upregulated in the SIDD cluster and
GPR15
mRNA was downregulated in the MDH cluster. mRNAs and lncRNAs that were differentially expressed in the MOD cluster were correlated among each other. Six lncRNAs and 120 mRNAs validated in the IMI DIRECT study. Using two-sample Mendelian randomisation, we found 52 mRNAs to have a causal effect on anthropometric traits (
n
=23) and lipid metabolism traits (
n
=10).
GPR146
showed a causal effect on plasma HDL-cholesterol levels (
p
= 2×10
–15
), without evidence for reverse causality.
Conclusions/interpretation
Multiple lncRNAs and mRNAs were found to be differentially expressed among clusters and particularly in the MOD cluster. mRNAs in the MOD cluster showed a possible causal effect on anthropometric traits, lipid metabolism traits and blood cell fractions. Together, our results show that individuals in the MOD cluster show aberrant RNA expression of genes that have a suggested causal role on multiple diabetes-relevant traits.
Graphical abstract
Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. ...Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.
Aims/hypothesis
We compared the ability of genetic (established type 2 diabetes, fasting glucose, 2 h glucose and obesity variants) and modifiable lifestyle (diet, physical activity, smoking, alcohol ...and education) risk factors to predict incident type 2 diabetes and obesity in a population-based prospective cohort of 3,444 Swedish adults studied sequentially at baseline and 10 years later.
Methods
Multivariable logistic regression analyses were used to assess the predictive ability of genetic and lifestyle risk factors on incident obesity and type 2 diabetes by calculating the AUC.
Results
The predictive accuracy of lifestyle risk factors was similar to that yielded by genetic information for incident type 2 diabetes (AUC 75% and 74%, respectively) and obesity (AUC 68% and 73%, respectively) in models adjusted for age, age
2
and sex. The addition of genetic information to the lifestyle model significantly improved the prediction of type 2 diabetes (AUC 80%;
p
= 0.0003) and obesity (AUC 79%;
p
< 0.0001) and resulted in a net reclassification improvement of 58% for type 2 diabetes and 64% for obesity.
Conclusions/interpretation
These findings illustrate that lifestyle and genetic information separately provide a similarly high degree of long-range predictive accuracy for obesity and type 2 diabetes.
Recent genome-wide meta-analyses identified 157 loci associated with cross-sectional lipid traits. Here we tested whether these loci associate (singly and in trait-specific genetic risk scores GRS) ...with longitudinal changes in total cholesterol (TC) and triglyceride (TG) levels in a population-based prospective cohort from Northern Sweden (the GLACIER Study). We sought replication in a southern Swedish cohort (the MDC Study; N = 2,943). GLACIER Study participants (N = 6,064) were genotyped with the MetaboChip array. Up to 3,495 participants had 10-yr follow-up data available in the GLACIER Study. The TC- and TG-specific GRSs were strongly associated with change in lipid levels (β = 0.02 mmol/l per effect allele per decade follow-up, P = 2.0 × 10(-11) for TC; β = 0.02 mmol/l per effect allele per decade follow-up, P = 5.0 × 10(-5) for TG). In individual SNP analysis, one TC locus, apolipoprotein E (APOE) rs4420638 (β = 0.12 mmol/l per effect allele per decade follow-up, P = 2.0 × 10(-5)), and two TG loci, tribbles pseudokinase 1 (TRIB1) rs2954029 (β = 0.09 mmol/l per effect allele per decade follow-up, P = 5.1 × 10(-4)) and apolipoprotein A-I (APOA1) rs6589564 (β = 0.31 mmol/l per effect allele per decade follow-up, P = 1.4 × 10(-8)), remained significantly associated with longitudinal changes for the respective traits after correction for multiple testing. An additional 12 loci were nominally associated with TC or TG changes. In replication analyses, the APOE rs4420638, TRIB1 rs2954029, and APOA1 rs6589564 associations were confirmed (P ≤ 0.001). In summary, trait-specific GRSs are robustly associated with 10-yr changes in lipid levels and three individual SNPs were strongly associated with 10-yr changes in lipid levels.
Aims/hypothesis
The association of single nucleotide polymorphisms (SNPs) proximal to
CRY2
and
MTNR1B
with fasting glucose is well established.
CRY1/2
and
MTNR1B
encode proteins that regulate ...circadian rhythmicity and influence energy metabolism. Here we tested whether season modified the relationship of these loci with blood glucose concentration.
Methods
SNPs rs8192440 (
CRY1
), rs11605924 (
CRY2
) and rs10830963 (
MTNR1B
) were genotyped in a prospective cohort study from northern Sweden (
n
= 16,499). The number of hours of daylight exposure during the year ranged from 4.5 to 22 h daily. Owing to the non-linear distribution of daylight throughout the year, season was dichotomised based on the vernal and autumnal equinoxes. Effect modification was assessed using linear regression models fitted with a SNP × season interaction term, marginal effect terms and putative confounding variables, with fasting or 2 h glucose concentrations as outcomes.
Results
The rs8192440 (
CRY1
) variant was only associated with fasting glucose among participants (
n
= 2,318) examined during the light season (
β
= −0.04 mmol/l per A allele, 95% CI −0.08, −0.01,
p
= 0.02,
p
interaction
= 0.01). In addition to the established association with fasting glucose, the rs11605924 (
CRY2
) and rs10830963 (
MTNR1B
) loci were associated with 2 h glucose concentrations (
β
= 0.07 mmol/l per A allele, 95% CI 0.03, 0.12,
p
= 0.0008,
n
= 9,605, and
β
= −0.11 mmol/l per G allele, 95% CI −0.15, −0.06,
p
< 0.0001,
n
= 9,517, respectively), but only in participants examined during the dark season (
p
interaction
= 0.006 and 0.04, respectively). Repeated measures analyses including data collected 10 years after baseline (
n
= 3,500) confirmed the results for the
CRY1
locus (
p
interaction
= 0.01).
Conclusions/interpretation
In summary, these observations suggest a biologically plausible season-dependent association between SNPs at
CRY1
,
CRY2
and
MTNR1B
and glucose homeostasis.
Middle Eastern immigrants exhibit high levels of physical inactivity and are at an increased risk for Type 2 diabetes. The primary aim of this study was to examine the changes in objectively assessed ...physical activity levels following a culturally adapted lifestyle intervention program. The secondary aim was to examine the association between objectively assessed physical activity and insulin sensitivity.
RCT conducted over 4 months in 2015.
Iraqi immigrants residing in Malmö, Sweden, exhibiting one or more risk factors for Type 2 diabetes.
The intervention group (n=50) was offered a culturally adapted lifestyle intervention comprising seven group sessions including a cooking class. The control group (n=46) received usual care.
Raw accelerometry data were processed by validated procedures and daily mean physical activity intensity, vector magnitude high-pass filtered (VM-HPF), was inferred. Further inferences into the number of hours/day spent in sedentary (VM-HPF <48 milli-Gs mGs where G=9.8 m/sec2) and light- (48– <163 mGs); moderate- (163– <420 mGs); and vigorous-intensity (≥420 mGs) activities were also calculated (year of analysis was 2016–2017).
No difference was observed between the two groups in terms of change over time in VM-HPF. There was a significant increase in the number of hours/day spent in light intensity physical activity in the intervention group compared with the control group (β=0.023, 95% CI=0.001, 0.045, p=0.037). The intervention group also increased the time spent in sedentary activities, with the highest VM-HPF (36– <48 mGs) within the sedentary behavior (B=0.022, 95% CI=0.002, 0.042, p=0.03). Higher VM-HPF was significantly associated with a higher insulin sensitivity index (β=0.014, 95% CI=0.0004, 0.025, p=0.007).
The findings favor the culturally adapted intervention approach for addressing low physical activity levels among Middle Eastern immigrants. Replacing sedentary time with light-intensity activities could be an achievable goal and will have potential beneficial effects for diabetes prevention among this sedentary group of immigrants.
This study was registered at www.clinicaltrials.gov NCT01420198.
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