Roelof A.J. Smit

Genetic Evidence for a Link Between Favorable Adiposity and Lower Risk of Type 2 Diabetes, Hypertension, and Heart Disease.

Recent genetic studies have identified some alleles that are associated with higher BMI but lower risk of type 2 diabetes, hypertension, and heart disease. These “favorable adiposity” alleles are collectively associated with lower insulin levels and higher subcutaneous–to–visceral adipose tissue ratio and may protect from disease through higher adipose storage capacity. We aimed to use data from 164,609 individuals from the UK Biobank and five other studies to replicate associations between a genetic score of 11 favorable adiposity variants and adiposity and risk of disease, to test for interactions between BMI and favorable adiposity genetics, and to test effects separately in men and women. In the UK Biobank, the 50% of individuals carrying the most favorable adiposity alleles had higher BMIs (0.120 kg/m2 [95% CI 0.066, 0.174]; P = 1E-5) and higher body fat percentage (0.301% [0.230, 0.372]; P = 1E-16) compared with the 50% of individuals carrying the fewest alleles. For a given BMI, the 50% of individuals carrying the most favorable adiposity alleles were at lower risk of type 2 diabetes (odds ratio [OR] 0.837 [0.784, 0.894]; P = 1E-7), hypertension (OR 0.935 [0.911, 0.958]; P = 1E-7), and heart disease (OR 0.921 [0.872, 0.973]; P = 0.003) and had lower blood pressure (systolic −0.859 mmHg [−1.099, −0.618]; P = 3E-12 and diastolic −0.394 mmHg [−0.534, −0.254]; P = 4E-8). In women, these associations could be explained by the observation that the alleles associated with higher BMI but lower risk of disease were also associated with a favorable body fat distribution, with a lower waist-to-hip ratio (−0.004 cm [95% CI −0.005, −0.003] 50% vs. 50%; P = 3E-14), but in men, the favorable adiposity alleles were associated with higher waist circumference (0.454 cm [0.267, 0.641] 50% vs. 50%; P = 2E-6) and higher waist-to-hip ratio (0.0013 [0.0003, 0.0024] 50% vs. 50%; P = 0.01). Results were strengthened when a meta-analysis with five additional studies was conducted. There was no evidence of interaction between a genetic score consisting of known BMI variants and the favorable adiposity genetic score. In conclusion, different molecular mechanisms that lead to higher body fat percentage (with greater subcutaneous storage capacity) can have different impacts on cardiometabolic disease risk. Although higher BMI is associated with higher risk of diseases, better fat storage capacity could reduce the risk.

Meta-analysis of genome-wide association studies of HDL cholesterol response to statins.

Background In addition to lowering low density lipoprotein cholesterol (LDL-C), statin therapy also raises high density lipoprotein cholesterol (HDL-C) levels. Inter-individual variation in HDL-C response to statins may be partially explained by genetic variation. Methods and results We performed a meta-analysis of genome-wide association studies (GWAS) to identify variants with an effect on statin-induced high density lipoprotein cholesterol (HDL-C) changes. The 123 most promising signals with p<1×10−4 from the 16 769 statin-treated participants in the first analysis stage were followed up in an independent group of 10 951 statin-treated individuals, providing a total sample size of 27 720 individuals. The only associations of genome-wide significance (p<5×10−8) were between minor alleles at the CETP locus and greater HDL-C response to statin treatment. Conclusions Based on results from this study that included a relatively large sample size, we suggest that CETP may be the only detectable locus with common genetic variants that influence HDL-C response to statins substantially in individuals of European descent. Although CETP is known to be associated with HDL-C, we provide evidence that this pharmacogenetic effect is independent of its association with baseline HDL-C levels.

Higher Visit-to-Visit Low-Density Lipoprotein Cholesterol Variability Is Associated With Lower Cognitive Performance, Lower Cerebral Blood Flow, and Greater White Matter Hyperintensity Load in Older Subjects.

Background: Recently, it was shown that intraindividual variation in low-density lipoprotein cholesterol (LDL-C) predicts both cerebrovascular and cardiovascular events. We aimed to examine whether this extends to cognitive function and examined possible pathways using a magnetic resonance imaging substudy. Methods: We investigated the association between LDL-C variability and 4 cognitive domains at month 30 in 4428 participants of PROSPER (PROspective Study of Pravastatin in the Elderly at Risk). Additionally, we assessed the association of LDL-C variability with neuroimaging outcomes in a subset of 535 participants. LDL-C variability was defined as the intraindividual standard deviation over 4 postbaseline LDL-C measurements, and all analyses were adjusted for mean LDL-C levels and cardiovascular risk factors. Results: Higher LDL-C variability was associated with lower cognitive function in both the placebo and pravastatin treatment arms. Associations were present for selective attention (P=0.017 and P=0.11, respectively), processing speed (P=0.20 and P=0.029), and memory (immediate recall, P=0.002 and P=0.006; delayed recall, P=0.001 and P⩽0.001). Furthermore, higher LDL-C variability was associated with lower cerebral blood flow in both trial arms (P=0.031 and P=0.050) and with greater white matter hyperintensity load in the pravastatin arm (P=0.046). No evidence was found for interaction between LDL-C variability and pravastatin treatment for both cognitive and magnetic resonance imaging outcomes. Conclusions: We found that higher visit-to-visit variability in LDL-C, independently of mean LDL-C levels and statin treatment, is associated with lower cognitive performance, lower cerebral blood flow, and greater white matter hyperintensity load.

Thyroid Signaling, Insulin Resistance, and 2 Diabetes Mellitus: A Mendelian Randomization Study

Context Increasing evidence suggests an association between thyroid-stimulating hormone (TSH), free thyroxine (fT4), and deiodinases with insulin resistance and type 2 diabetes mellitus (T2D). Objective We examined whether TSH and fT4 levels and deiodinases are causally associated with insulin resistance and T2D, using Mendelian randomization. Methods We selected 20 genetic variants for TSH level and four for fT4 level (identified in a genome-wide association study (GWAS) meta-analysis of European-ancestry cohorts) as instrumental variables for TSH and fT4 levels, respectively. We used summary data from GWASs on the outcomes T2D [Diabetes, Genetics Replication and Meta-analysis (DIAGRAM), n = 12,171 cases and n = 56,862 control subjects] and glycemic traits in patients without diabetes [Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC), n = 46,186 for fasting glucose and insulin and n = 46,368 for hemoglobin A1c]. To examine whether the associations between TSH/fT4 levels and the study outcomes were causal, we combined the effects of the genetic instruments. Furthermore, we examined the associations among 16 variants in DIO1, DIO2, DIO3, and T2D and glycemic traits. Results We found no evidence for an association between the combined genetic instrumental variables for TSH and fT4 and the study outcomes. For example, we did not observe a genetically determined association between high TSH level and T2D (odds ratio, 0.91 per standard deviation TSH increase; 95% confidence interval, 0.78 to 1.07). Selected genetic variants in DIO1 (e.g., rs7527713) were associated with measures of insulin resistance. Conclusion We found no evidence for a causal association between circulatory levels of TSH and fT4 with insulin resistance and T2D, but we found suggestive evidence that DIO1 affects glucose metabolism.

Using genetic variation for establishing causality of cardiovascular risk factors: overcoming confounding and reverse causality

Cardiovascular disease (CVD) remains the leading cause of death in developed countries, despite the decline of CVD mortality over the last two decades. From observational, predictive research, efforts have been made to find causal risk factors for CVD. However, in recent years, some of these findings have been shown to be mistaken. Possible explanations for the discrepant findings are confounding and reverse causation. Genetic epidemiology has tried to address these problems through the use of Mendelian randomisation. In this paper, we discuss the promise and limitations of using genetic variation for establishing causality of cardiovascular risk factors.

Lower Blood Pressure and Apathy Coincide in Older Persons with Poorer Functional Ability: The Discontinuation of Antihypertensive Treatment in Elderly People (DANTE) Study Leiden

To examine the association between blood pressure (BP) measures and symptoms of apathy and depression in older adults with various levels of functional ability.

Visit-to-visit blood pressure variability and future functional decline in old age.

Objective: Higher blood pressure variability (BPV), independent of mean blood pressure (BP), has been associated with adverse health outcomes. We investigated the association between visit-to-visit BPV and functional decline in older adults at high cardiovascular risk. Methods: In PROspective Study of Pravastatin in the Elderly at Risk, 4745 participants with mean age of 75.2 years and high cardiovascular risk were followed for a mean of 3.2 years. BP was measured in every 3 months during the first 18 months. BPV was defined as the intraindividual SD of measurements across these visits. Functional status in basic and instrumental activities of daily living was measured using the Barthel (ADL) and Lawton (IADL) scales, first at 18 months and then during follow-up until 48 months. Functional decline was calculated over this period. Results: BPV was not cross-sectionally associated with functional status at 18 months. Higher SBPV was associated with steeper functional decline, whereas DBPV was not. Each 10 mmHg higher SBPV was associated with a 0.064 (95% confidence interval 0.016–0.112, P = 0.009) annual decline in ADL score and with a 0.078 decline (95% confidence interval 0.020–0.136, P = 0.008) in IADL score. These associations were not modified by sex, hypertension or antihypertensives. These findings were independent of mean BP, cardiovascular risk factors and morbidities and cognition. Conclusion: Higher visit-to-visit SBPV but not DBPV was associated with steeper functional decline in older adults at high cardiovascular risk. Higher SBPV is a novel risk factor for functional decline.

Increasing HDL-C levels with medication: current perspectives

Purpose of review To date, observational studies have repeatedly demonstrated an inverse association between HDL cholesterol (HDL-C) levels and cardiovascular outcomes. Although the efficacy of established HDL-modifying treatment strategies have been examined in multiple large-scale phase III trials, findings from these experimental studies conflict with the hypothesis that HDL-C levels are atheroprotective. In this review, we describe the trial evidence to date, and attempt to place these results in the broader context of recent hypotheses for the association between HDL-C levels and clinical outcomes. Recent findings Both translational and genetic studies are in line with the hypothesis that HDL-C levels do not hold causal importance for cardiovascular risk reduction. In addition to its possible role as a biomarker for other atherogenic lipoproteins, efforts should be made to elucidate HDLs’ role in lipoprotein flux, which is increasingly being linked to surrogate outcomes of importance to cardiovascular epidemiology. In the future, it will be of great importance to link this measure of HDL functionality to clinical endpoints. Summary Although trial evidence does not support an atheroprotective role of overall HDL-C plasma levels, HDL function/lipoprotein flux holds great promise for the development of novel therapeutic approaches.

Thyroid Status and Mortality Risk in Older Adults With Normal Thyrotropin: Sex Differences in the Milan Geriatrics 75+ Cohort Study

Background Thresholds of optimal thyroid status in old age are controversial. We investigated the longitudinal association between thyroid parameters and 10-year all-cause mortality risk in older outpatients with normal thyrotropin (TSH) and modification by sex and age. Methods Baseline TSH, free thyroxine (fT4), and free triiodothyronine (fT3) were assessed in the Milan Geriatrics 75+ Cohort Study. 324 men and 609 women older than 75 years had normal TSH. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for the associations between thyroid parameters and mortality risk using Cox regression. Sex-stratified analyses were adjusted for sociodemographic factors and comorbidities. Results 233 men and 367 women died during follow-up. After adjustment, each 1-mU/L higher TSH was associated with decreased mortality risk in men (HR 0.83, 95% CI 0.69-0.98), but not in women (HR 1.09, 95% CI 0.95-1.24) (p for sex interaction = .006). Each 1-ng/L higher fT4 was associated with increased mortality risk in men (HR 1.11, 95% CI 1.02-1.22), but not in women (HR 0.98, 95% CI 0.93-1.04) (p for sex interaction = .013). Each 1-pg/mL higher fT3 was associated with decreased mortality risk in women (HR 0.77, 95% CI 0.60-0.98), but not in men (HR 0.80, 95% CI 0.57-1.13). The inverse association between TSH and mortality was most pronounced in men older than 85 years. Conclusions Among older outpatients with normal TSH, higher TSH and lower fT4 were associated with decreased mortality risk in men but not in women. When assessing thyroid status, sex and age should be taken into account.

A critical appraisal of pharmacogenetic inference

In essence, pharmacogenetic research is aimed at discovering variants of importance to gene‐treatment interaction. However, epidemiological studies are rarely set up with this goal in mind. It is therefore of great importance that researchers clearly communicate which assumptions they have had to make, and which inherent limitations apply to the interpretation of their results. This review discusses considerations of, and the underlying assumptions for, utilizing different response phenotypes and study designs popular in pharmacogenetic research to infer gene‐treatment interaction effects, with a special focus on those dealing with of clinical effects of drug treatment.