Abstract
Statistical correction for measurement error in epidemiologic studies is possible, provided that information about the measurement error model and its parameters are available. Such ...information is commonly obtained from a randomly sampled internal validation sample. It is however unknown whether randomly sampling the internal validation sample is the optimal sampling strategy. We conducted a simulation study to investigate various internal validation sampling strategies in conjunction with regression calibration. Our simulation study showed that for an internal validation study sample of 40% of the main study’s sample size, stratified random and extremes sampling had a small efficiency gain over random sampling (10% and 12% decrease on average over all scenarios, respectively). The efficiency gain was more pronounced in smaller validation samples of 10% of the main study’s sample size (i.e., a 31% and 36% decrease on average over all scenarios, for stratified random and extremes sampling, respectively). To mitigate the bias due to measurement error in epidemiologic studies, small efficiency gains can be achieved for internal validation sampling strategies other than random, but only when measurement error is nondifferential. For regression calibration, the gain in efficiency is, however, at the cost of a higher percentage bias and lower coverage.
Clinical epidemiological studies investigate whether an exposure, or risk factor, is causally related to the development or progression of a disease or mortality. It might be of interest to study ...whether this relation is different in different types of patients. To address such research questions, the presence of interaction among risk factors can be examined. Causal interaction between two risk factors is considered most clinically relevant in epidemiology. Causal interaction occurs when two risk factors act together in causing disease and is explicitly defined as a deviation from additivity on a risk difference scale. Statistical interaction can be evaluated on both an additive (absolute risk) and multiplicative (relative risk) scale, depending on the model that is used. When using logistic regression models, which are multiplicative models, several measures of additive interaction are presented to evaluate whether the magnitude of an association differs across subgroups: the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP), or the synergy index (S). For a transparent presentation of interaction effects the recent Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement advises reporting the separate effect of each exposure as well as the joint effect compared with the unexposed group as a joint reference category to permit evaluation of both additive and multiplicative interaction.
The prevalence of metabolic syndrome varies among populations with different ethnicities. Asian populations develop metabolic complications at lower amounts of adiposity than western populations. The ...role of abdominal obesity in the metabolic differences between the two populations is poorly understood.
Our objectives were to estimate the prevalence of metabolic syndrome and the relative contribution of its components in the Indonesian and the Dutch population, as well as to examine the associations of overall and abdominal obesity with metabolic syndrome.
In this cross-sectional study of middle-aged adults in the Netherlands Epidemiology of Obesity Study (n = 6602) and the Indonesian National Health Surveillance (n = 10,575), metabolic syndrome was defined by the unified IDF and AHA/NHLBI criteria. We performed logistic and linear regressions to examine associations of BMI and waist circumference with the metabolic syndrome, mutually adjusted for waist circumference and BMI.
The prevalence of metabolic syndrome was 28% and 46% in Indonesian men and women, and 36% and 24% in Dutch men and women. The most prominent components were hypertension (61%) and hyperglycemia (51%) in the Indonesian, and hypertension (62%) and abdominal obesity (40%) in the Dutch population. Per SD in BMI and waist circumference, odds ratios (ORs, 95% CI) of metabolic syndrome were 1.5 (1.3-1.8) and 2.3 (1.9-2.7) in Indonesian men and 1.7 (1.2-2.5) and 2.9 (2.1-4.1) in Dutch men. The ORs of metabolic syndrome were 1.4 (1.2-1.6) and 2.3 (2.0-2.7) in Indonesian women and 1.0 (0.8-1.3) and 4.2 (3.2-5.4) in Dutch women.
More Indonesian women than men have metabolic syndrome, whereas the opposite is true for the Dutch population. In both the Indonesian and the Dutch populations, hypertension is the primary contributor to the prevalence of metabolic syndrome. In both populations, abdominal adiposity was more strongly associated with metabolic syndrome than overall adiposity.
Changes in endothelial glycocalyx are one of the earliest changes in development of cardiovascular disease. The endothelial glycocalyx is both an important biological modifier of interactions between ...flowing blood and the vessel wall, and a determinant of organ perfusion. We hypothesize that deeper penetration of erythrocytes into the glycocalyx is associated with reduced microvascular perfusion. The population-based prospective cohort study (the Netherlands Epidemiology of Obesity NEO study) includes 6,673 middle-aged individuals (oversampling of overweight and obese individuals). Within this cohort, we have imaged the sublingual microvasculature of 915 participants using sidestream darkfield (SDF) imaging together with a recently developed automated acquisition and analysis approach. Presence of RBC (as a marker of microvascular perfusion) and perfused boundary region (PBR), a marker for endothelial glycocalyx barrier properties for RBC accessibility, were assessed in vessels between 5 and 25 µm RBC column width. A wide range of variability in PBR measurements, with a mean PBR of 2.14 µm (range: 1.43-2.86 µm), was observed. Linear regression analysis showed a marked association between PBR and microvascular perfusion, reflected by RBC filling percentage (regression coefficient β: -0.034; 95% confidence interval: -0.037 to -0.031). We conclude that microvascular beds with a thick ("healthy") glycocalyx (low PBR), reflects efficient perfusion of the microvascular bed. In contrast, a thin ("risk") glycocalyx (high PBR) is associated with a less efficient and defective microvascular perfusion.
Transgender adolescents aspiring to have the body characteristics of the affirmed sex can receive hormonal treatment. However, it is unknown how body shape and composition develop during treatment ...and whether transgender persons obtain the desired body phenotype.
To examine the change in body shape and composition from the start of treatment with gonadotropin-releasing hormone agonists (GnRHa) until 22 years of age and to compare these measurements at 22 years with those of age-matched peers.
71 transwomen (birth-assigned boys) and 121 transmen (birth-assigned girls) who started treatment from 1998 through 2014 were included in this retrospective study. GnRHa treatment was started and cross-sex hormonal treatment was added at 16 years of age. Anthropometric and whole-body dual-energy x-ray absorptiometry data were retrieved from medical records. Linear mixed model regression was performed to examine changes over time. SD scores (SDS) were calculated to compare body shape and composition with those of age-matched peers.
Change in waist-hip ratio (WHR), total body fat (TBF), and total lean body mass (LBM) during hormonal treatment. SDS of measures of body shape and composition compared with age-matched peers at 22 years of age.
In transwomen, TBF increased (+10%, 95% CI = 7–11) while total LBM (−10%, 95% CI = −11 to −7) and WHR (−0.04, 95% CI = −0.05 to −0.02) decreased. Compared with ciswomen, SDS at 22 years of age were +0.3 (95% CI = 0.0–0.5) for WHR, and 0.0 (95% CI = −0.2 to 0.3) for TBF. Compared with cismen, SDS were −1.0 (95% CI = −1.3 to −0.7) for WHR, and +2.2 (95% CI = 2.2–2.4) for TBF. In transmen, TBF decreased (−3%, 95% CI = −4 to −1), while LBM (+3%, 95% CI = 1–4) and WHR (+0.03, 95% CI = 0.01–0.04) increased. Compared with ciswomen, SDS at 22 years of age were +0.6 (95% CI = 0.4–0.8) for WHR, and −1.1 (95% CI = −1.4 to −0.9) for TBF. Compared with cismen, SDS were −0.5 (95% CI = −0.8 to −0.3) for WHR, and +1.8 (95% CI = 1.6–1.9) for TBF.
Knowing body shape and composition outcomes at 22 years of age will help care providers in counseling transgender youth on expectations of attaining the desired body phenotype.
This study presents the largest group of transgender adults to date who started treatment in their teens. Despite missing data, selection bias was not found.
During treatment, WHR and body composition changed toward the affirmed sex. At 22 years of age, transwomen compared better to age-matched ciswomen than to cismen, whereas transmen were between reference values for ciswomen and cismen.
Klaver M, de Mutsert R, Wiepjes CM, et al. Early Hormonal Treatment Affects Body Composition and Body Shape in Young Transgender Adolescents. J Sex Med 2018;15:251–260.
To evaluate whether the association between plasma branched-chain amino acids (BCAA) and intrahepatic lipid (IHL) was affected by physical activity level. Furthermore, to investigate if a ...conventional exercise training program, a subcategory of physical activity, could lower plasma BCAA along with alterations in IHL content in patients with type 2 diabetes (T2DM) and people with nonalcoholic fatty liver (NAFL).
To investigate the effect of physical activity on the association between plasma BCAA and IHL content, linear regression analyses were performed in 1983 individuals from the Netherlands Epidemiology of Obesity (NEO) stratified by physical activity frequency. Furthermore, the effect of a 12-week supervised combined aerobic resistance-exercise program on plasma BCAA, insulin sensitivity (hyperinsulinemic-euglycemic clamp), and IHL (proton-magnetic resonance spectroscopy (
H-MRS)) was investigated in seven patients with T2DM, seven individuals with NAFL and seven BMI-matched control participants (CON).
We observed positive associations between plasma valine, isoleucine and leucine level, and IHL content (1.29 (95% CI: 1.21, 1.38), 1.52 (95% CI: 1.43, 1.61), and 1.54 (95% CI: 1.44, 1.64) times IHL, respectively, per standard deviation of plasma amino acid level). Similar associations were observed in less active versus more active individuals. Exercise training did not change plasma BCAA levels among groups, but reduced IHL content in NAFL (from 11.6 ± 3.0% pre-exercise to 8.1 ± 2.0% post exercise, p < 0.05) and CON (from 2.4 ± 0.6% pre-exercise to 1.6 ± 1.4% post exercise, p < 0.05), and improved peripheral insulin sensitivity in NAFL as well by ~23% (p < 0.05).
The association between plasma BCAA levels and IHL is not affected by physical activity level. Exercise training reduced IHL without affecting plasma BCAA levels in individuals with NAFL and CON. We conclude that exercise training-induced reduction in IHL content is not related to changes in plasma BCAA levels.
Trial registry number: NCT01317576.
Little is known about the contribution of genetic variation to food timing, and breakfast has been determined to exhibit the most heritable meal timing. As breakfast timing and skipping are not ...routinely measured in large cohort studies, alternative approaches include analyses of correlated traits.
The aim of this study was to elucidate breakfast skipping genetic variants through a proxy-phenotype genome-wide association study (GWAS) for breakfast cereal skipping, a commonly assessed correlated trait.
We leveraged the statistical power of the UK Biobank (n = 193,860) to identify genetic variants related to breakfast cereal skipping as a proxy-phenotype for breakfast skipping and applied several in silico approaches to investigate mechanistic functions and links to traits/diseases. Next, we attempted validation of our approach in smaller breakfast skipping GWAS from the TwinUK (n = 2,006) and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium (n = 11,963).
In the UK Biobank, we identified 6 independent GWAS variants, including those implicated for caffeine (ARID3B/CYP1A1), carbohydrate metabolism (FGF21), schizophrenia (ZNF804A), and encoding enzymes important for N6-methyladenosine RNA transmethylation (METTL4, YWHAB, and YTHDF3), which regulates the pace of the circadian clock. Expression of identified genes was enriched in the cerebellum. Genome-wide correlation analyses indicated positive correlations with anthropometric traits. Through Mendelian randomization (MR), we observed causal links between genetically determined breakfast skipping and higher body mass index, more depressive symptoms, and smoking. In bidirectional MR, we demonstrated a causal link between being an evening person and skipping breakfast, but not vice versa. We observed association of our signals in an independent breakfast skipping GWAS in another British cohort (P = 0.032), TwinUK, but not in a meta-analysis of non-British cohorts from the CHARGE consortium (P = 0.095).
Our proxy-phenotype GWAS identified 6 genetic variants for breakfast skipping, linking clock regulation with food timing and suggesting a possible beneficial role of regular breakfast intake as part of a healthy lifestyle.
Nine hereditary neurodegenerative diseases are known as polyglutamine diseases, including Huntington disease, 6 spinocerebellar ataxias (SCAs) (SCA1, SCA2, SCA3, SCA6, SCA7, and SCA17), ...dentatorubral-pallidoluysion atrophy, and spinal bulbar muscular atrophy.
To determine the prevalence of carriers of intermediate and pathological polyglutamine disease-associated alleles among the general population.
This observational cross-sectional study included data from 5 large European population-based cohorts that were compiled between 1997 and 2012, and the analyses were conducted in 2018. In total, 16 547 DNA samples were obtained from participants of the 5 cohorts. Individuals with a lifetime diagnosis of major depression were excluded (n = 2351). In the remaining 14 196 participants without an established polyglutamine disease diagnosis, the CAG repeat size in both alleles of all 9 polyglutamine disease-associated genes (PDAGs) (ie, ATXN1, ATXN2, ATXN3, CACNA1A, ATXN7, TBP, HTT, ATN1, and AR) was determined.
The number of CAG repeats in the alleles of the 9 PDAGs.
The number of individuals with alleles within the intermediate or pathological range per PDAG, as well as differences in sex, age, and body mass index between individuals carrying alleles within the normal or intermediate range and individuals carrying alleles within the pathological range of PDAGs.
In the 14 196 analyzed participants (age range, 18-99 years; 56.3% female), 10.7% had a CAG repeat number within the intermediate range of at least 1 PDAG. Moreover, up to 1.3% of the participants had a CAG repeat number within the disease-causing range, predominantly in the lower pathological range associated with elderly onset. No differences in sex, age, or body mass index were found between individuals with CAG repeat numbers within the pathological range and individuals with CAG repeat numbers within the normal or intermediate range.
These results indicate a high prevalence of individuals carrying intermediate and pathological ranges of polyglutamine disease-associated alleles among the general population. Therefore, a substantially larger proportion of individuals than previously estimated may be at risk of developing a polyglutamine disease later in life or bearing children with a de novo mutation.
Dietary macronutrient composition may affect hepatic liver content and its associated diseases, but the results from human intervention trials have been equivocal or underpowered. We aimed to assess ...the effects of dietary macronutrient composition on liver fat content by conducting a systematic review and meta-analysis of randomized controlled trials in adults. Four databases (PubMed, Embase, Web of Science, and COCHRANE Library) were systematically searched for trials with isocaloric diets evaluating the effect of dietary macronutrient composition (energy percentages of fat, carbohydrates, and protein, and their specific types) on liver fat content as assessed by magnetic resonance techniques, computed tomography or liver biopsy. Data on change in liver fat content were pooled by random or fixed-effects meta-analyses and expressed as standardized mean difference (SMD). We included 26 randomized controlled trials providing data for 32 comparisons on dietary macronutrient composition. Replacing dietary fat with carbohydrates did not result in changes in liver fat (12 comparisons, SMD 0.01 (95% CI -0.36; 0.37)). Unsaturated fat as compared with saturated fat reduced liver fat content (4 comparisons, SMD -0.80 (95% CI -1.09; -0.51)). Replacing carbohydrates with protein reduced liver fat content (5 comparisons, SMD -0.33 (95% CI -0.54; -0.12)). Our meta-analyses showed that replacing carbohydrates with total fat on liver fat content was not effective, while replacing carbohydrates with proteins and saturated fat with unsaturated fat was. More well-performed and well-described studies on the effect of types of carbohydrates and proteins on liver fat content are needed, especially studies comparing proteins with fats.
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