Annika Tillander

Vagotomy and Parkinson disease A Swedish register–based matched-cohort study

Objective: To examine whether vagotomy decreases the risk of Parkinson disease (PD). Methods: Using data from nationwide Swedish registers, we conducted a matched-cohort study of 9,430 vagotomized patients (3,445 truncal and 5,978 selective) identified between 1970 and 2010 and 377,200 reference individuals from the general population individually matched to vagotomized patients by sex and year of birth with a 40:1 ratio. Participants were followed up from the date of vagotomy until PD diagnosis, death, emigration out of Sweden, or December 31, 2010, whichever occurred first. Vagotomy and PD were identified from the Swedish Patient Register. We estimated hazard ratios (HRs) with 95% confidence intervals (CIs) using Cox models stratified by matching variables, adjusting for country of birth, chronic obstructive pulmonary disease, diabetes mellitus, vascular diseases, rheumatologic disease, osteoarthritis, and comorbidity index. Results: A total of 4,930 cases of incident PD were identified during 7.3 million person-years of follow-up. PD incidence (per 100,000 person-years) was 61.8 among vagotomized patients (80.4 for truncal and 55.1 for selective) and 67.5 among reference individuals. Overall, vagotomy was not associated with PD risk (HR 0.96, 95% CI 0.78–1.17). However, there was a suggestion of lower risk among patients with truncal vagotomy (HR 0.78, 95% CI 0.55–1.09), which may be driven by truncal vagotomy at least 5 years before PD diagnosis (HR 0.59, 95% CI 0.37–0.93). Selective vagotomy was not related to PD risk in any analyses. Conclusions: Although overall vagotomy was not associated the risk of PD, we found suggestive evidence for a potential protective effect of truncal, but not selective, vagotomy against PD development.

Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology.

Asthma, hay fever (or allergic rhinitis) and eczema (or atopic dermatitis) often coexist in the same individuals, partly because of a shared genetic origin. To identify shared risk variants, we performed a genome-wide association study (GWAS; n = 360,838) of a broad allergic disease phenotype that considers the presence of any one of these three diseases. We identified 136 independent risk variants (P < 3 × 10−8), including 73 not previously reported, which implicate 132 nearby genes in allergic disease pathophysiology. Disease-specific effects were detected for only six variants, confirming that most represent shared risk factors. Tissue-specific heritability and biological process enrichment analyses suggest that shared risk variants influence lymphocyte-mediated immunity. Six target genes provide an opportunity for drug repositioning, while for 36 genes CpG methylation was found to influence transcription independently of genetic effects. Asthma, hay fever and eczema partly coexist because they share many genetic risk variants that dysregulate the expression of immune-related genes.

Exploring the Causal Pathway from Telomere Length to Coronary Heart Disease: A Network Mendelian Randomization Study

Rationale: Observational studies have found shorter leukocyte telomere length (TL) to be a risk factor for coronary heart disease (CHD), and recently the association was suggested to be causal. However, the relationship between TL and common metabolic risk factors for CHD is not well understood. Whether these risk factors could explain pathways from TL to CHD warrants further attention. Objective: To examine whether metabolic risk factors for CHD mediate the causal pathway from short TL to increased risk of CHD using a network Mendelian randomization design. Methods and Results: Summary statistics from several genome-wide association studies were used in a 2-sample Mendelian randomization study design. Network Mendelian randomization analysis—an approach using genetic variants as the instrumental variables for both the exposure and mediator to infer causality—was performed to examine the causal association between telomeres and CHD and metabolic risk factors. Summary statistics from the ENGAGE Telomere Consortium were used (n=37 684) as a TL genetic instrument, CARDIoGRAMplusC4D Consortium data were used (case=22 233 and control=64 762) for CHD, and other consortia data were used for metabolic traits (fasting insulin, triglyceride, total cholesterol, low-density lipoprotein cholesterol, fasting glucose, diabetes mellitus, glycohemoglobin, body mass index, waist circumference, and waist:hip ratio). One-unit increase of genetically determined TL was associated with −0.07 (95% confidence interval, −0.01 to −0.12; P=0.01) lower log-transformed fasting insulin (pmol/L) and 21% lower odds (95% confidence interval, 3–35; P=0.02) of CHD. Higher genetically determined log-transformed fasting insulin level was associated with higher CHD risk (odds ratio, 1.86; 95% confidence interval, 1.01–3.41; P=0.04). Conclusions: Overall, our findings support a role of insulin as a mediator on the causal pathway from shorter telomeres to CHD pathogenesis.

Broad HIV-1 inhibition in vitro by vaccine-elicited CD8+ T cells in African adults

We are developing a pan-clade HIV-1 T-cell vaccine HIVconsv, which could complement Env vaccines for prophylaxis and be a key to HIV cure. Our strategy focuses vaccine-elicited effector T-cells on functionally and structurally conserved regions (not full-length proteins and not only epitopes) of the HIV-1 proteome, which are common to most global variants and which, if mutated, cause a replicative fitness loss. Our first clinical trial in low risk HIV-1-negative adults in Oxford demonstrated the principle that naturally mostly subdominant epitopes, when taken out of the context of full-length proteins/virus and delivered by potent regimens involving combinations of simian adenovirus and poxvirus modified vaccinia virus Ankara, can induce robust CD8+ T cells of broad specificities and functions capable of inhibiting in vitro HIV-1 replication. Here and for the first time, we tested this strategy in low risk HIV-1-negative adults in Africa. We showed that the vaccines were well tolerated and induced high frequencies of broadly HIVconsv-specific plurifunctional T cells, which inhibited in vitro viruses from four major clades A, B, C, and D. Because sub-Saharan Africa is globally the region most affected by HIV-1/AIDS, trial HIV-CORE 004 represents an important stage in the path toward efficacy evaluation of this highly rational and promising vaccine strategy.

Covariance structure approximation via gLasso in high-dimensional supervised classification

Recent work has shown that the Lasso-based regularization is very useful for estimating the high-dimensional inverse covariance matrix. A particularly useful scheme is based on penalizing the ℓ1 norm of the off-diagonal elements to encourage sparsity. We embed this type of regularization into high-dimensional classification. A two-stage estimation procedure is proposed which first recovers structural zeros of the inverse covariance matrix and then enforces block sparsity by moving non-zeros closer to the main diagonal. We show that the block-diagonal approximation of the inverse covariance matrix leads to an additive classifier, and demonstrate that accounting for the structure can yield better performance accuracy. Effect of the block size on classification is explored, and a class of asymptotically equivalent structure approximations in a high-dimensional setting is specified. We suggest a variable selection at the block level and investigate properties of this procedure in growing dimension asymptotics. We present a consistency result on the feature selection procedure, establish asymptotic lower an upper bounds for the fraction of separative blocks and specify constraints under which the reliable classification with block-wise feature selection can be performed. The relevance and benefits of the proposed approach are illustrated on both simulated and real data.

Body mass index in relation to serum prostate-specific antigen levels and prostate cancer risk.

High Body mass index (BMI) has been directly associated with risk of aggressive or fatal prostate cancer. One possible explanation may be an effect of BMI on serum levels of prostate‐specific antigen (PSA). To study the association between BMI and serum PSA as well as prostate cancer risk, a large cohort of men without prostate cancer at baseline was followed prospectively for prostate cancer diagnoses until 2015. Serum PSA and BMI were assessed among 15,827 men at baseline in 2010–2012. During follow‐up, 735 men were diagnosed with prostate cancer with 282 (38.4%) classified as high‐grade cancers. Multivariable linear regression models and natural cubic linear regression splines were fitted for analyses of BMI and log‐PSA. For risk analysis, Cox proportional hazards regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) and natural cubic Cox regression splines producing standardized cancer‐free probabilities were fitted. Results showed that baseline Serum PSA decreased by 1.6% (95% CI: −2.1 to −1.1) with every one unit increase in BMI. Statistically significant decreases of 3.7, 11.7 and 32.3% were seen for increasing BMI‐categories of 25 < 30, 30 < 35 and ≥35 kg/m2, respectively, compared to the reference (18.5 < 25 kg/m2). No statistically significant associations were seen between BMI and prostate cancer risk although results were indicative of a positive association to incidence rates of high‐grade disease and an inverse association to incidence of low‐grade disease. However, findings regarding risk are limited by the short follow‐up time. In conclusion, BMI was inversely associated to PSA‐levels. BMI should be taken into consideration when referring men to a prostate biopsy based on serum PSA‐levels.

Bayesian Block-Diagonal Predictive Classifier for Gaussian Data

The paper presents a method for constructing Bayesian predictive classifier in a high-dimensional setting. Given that classes are represented by Gaussian distributions with block-structured covariance matrix, a closed form expression for the posterior predictive distribution of the data is established. Due to factorization of this distribution, the resulting Bayesian predictive and marginal classifier provides an efficient solution to the high-dimensional problem by splitting it into smaller tractable problems. In a simulation study we show that the suggested classifier outperforms several alternative algorithms such as linear discriminant analysis based on block-wise inverse covariance estimators and the shrunken centroids regularized discriminant analysis.

Adherence to dietary recommendations for Swedish adults across categories of greenhouse gas emissions from food

OBJECTIVE To explore associations between diet-related greenhouse gas emissions (GHGE), nutrient intakes and adherence to the Nordic Nutrition Recommendations among Swedish adults. DESIGN Diet was assessed by 4d food records in the Swedish National Dietary Survey. GHGE was estimated by linking all foods to carbon dioxide equivalents, using data from life cycle assessment studies. Participants were categorized into quartiles of energy-adjusted GHGE and differences between GHGE groups regarding nutrient intakes and adherence to nutrient recommendations were explored. SETTING Sweden. SUBJECTS Women (n 840) and men (n 627) aged 18-80 years. RESULTS Differences in nutrient intakes and adherence to nutrient recommendations between GHGE groups were generally small. The dietary intake of participants with the lowest emissions was more in line with recommendations regarding protein, carbohydrates, dietary fibre and vitamin D, but further from recommendations regarding added sugar, compared with the highest GHGE group. The overall adherence to recommendations was found to be better among participants with lower emissions compared with higher emissions. Among women, 27 % in the lowest GHGE group adhered to at least twenty-three recommendations compared with only 12 % in the highest emission group. For men, the corresponding figures were 17 and 10 %, respectively. CONCLUSIONS The study compared nutrient intakes as well as adherence to dietary recommendations for diets with different levels of GHGE from a national dietary survey. We found that participants with low-emission diets, despite higher intake of added sugar, adhered to a larger number of dietary recommendations than those with high emissions.

Early-Life Factors and Risk of Parkinson’s Disease: A Register-Based Cohort Study

Parkinson’s disease (PD) may take decades to develop and early life exposures such as infection may be important. However, few epidemiological studies have evaluated early life risk factors in relation to PD risk. We therefore examined such associations in a prospective analysis of 3 545 612 individuals born in Sweden between 1932 and 1970 without PD on January 1, 2002. Incident PD cases were identified using the Swedish Patient Register during 2002–2010. Information on sibship size, number of older and younger siblings, multiple births, parental age, birth month and season was obtained from the Swedish Multi-Generation Register. Monthly data on national burden of influenza-like illness during 1932–1970 were obtained from the Swedish Public Health Agency. Hazard ratios with 95% confidence intervals (CI) were estimated using Cox proportional hazards regression. During the follow-up, 8779 incident PD cases were identified. As expected, older age, male sex, parental occupation as farmers, and family history of PD were associated with higher PD risk. Overall, early life factors, including flu burden in the year of birth, were not associated with PD risk, although we did find a lower PD risk among participants with older siblings than those without (HR = 0.93, 95%CI: 0.89, 0.98). Our study therefore provided little support for important etiological contributions of early life factors to the PD risk late in life. The finding of a lower PD risk among individuals with older siblings will need confirmation and further investigation.

Effect of data discretization on the classification accuracy in a high-dimensional framework

We investigate discretization of continuous variables for classification problems in a high‐ dimensional framework. As the goal of classification is to correctly predict a class membership of an observation, we suggest a discretization method that optimizes the discretization procedure using the misclassification probability as a measure of the classification accuracy. Our method is compared to several other discretization methods as well as result for continuous data. To compare performance we consider three supervised classification methods, and to capture the effect of high dimensionality we investigate a number of feature variables for a fixed number of observations. Since discretization is a data transformation procedure, we also investigate how the dependence structure is affected by this. Our method performs well, and lower misclassification can be obtained in a high‐dimensional framework for both simulated and real data if the continuous feature variables are first discretized. The dependence structure is well maintained for some discretization methods.