Circulating miRNA Signature Predicts Cancer Incidence in Lynch Syndrome-A Pilot Study.

Lynch syndrome (LS) is the most common autosomal dominant cancer syndrome and is characterized by high genetic cancer risk modified by lifestyle factors. This study explored whether a circulating miRNA (c-miR) signature predicts LS cancer incidence within a 4-year prospective surveillance period. To gain insight how lifestyle behavior could affect LS cancer risk, we investigated whether the cancer-predicting c-miR signature correlates with known risk-reducing factors such as physical activity, body mass index (BMI), dietary fiber, or NSAID usage. The study included 110 c-miR samples from LS carriers, 18 of whom were diagnosed with cancer during a 4-year prospective surveillance period. Lasso regression was utilized to find c-miRs associated with cancer risk. Individual risk sum derived from the chosen c-miRs was used to develop a model to predict LS cancer incidence. This model was validated using 5-fold cross-validation. Correlation and pathway analyses were applied to inspect biological functions of c-miRs. Pearson correlation was used to examine the associations of c-miR risk sum and lifestyle factors. hsa-miR-10b-5p, hsa-miR-125b-5p, hsa-miR-200a-3p, hsa-miR-3613-5p, and hsa-miR-3615 were identified as cancer predictors by Lasso, and their risk sum score associated with higher likelihood of cancer incidence (HR 2.72, 95% confidence interval: 1.64-4.52, C-index = 0.72). In cross-validation, the model indicated good concordance with the average C-index of 0.75 (0.6-1.0). Coregulated hsa-miR-10b-5p, hsa-miR-125b-5p, and hsa-miR-200a-3p targeted genes involved in cancer-associated biological pathways. The c-miR risk sum score correlated with BMI (r = 0.23, P < 0.01). In summary, BMI-associated c-miRs predict LS cancer incidence within 4 years, although further validation is required. PREVENTION RELEVANCE: The development of cancer risk prediction models is key to improving the survival of patients with LS. This pilot study describes a serum miRNA signature-based risk prediction model that predicts LS cancer incidence within 4 years, although further validation is required.

Menopausal symptoms and cardiometabolic risk factors in middle-aged women: A cross-sectional and longitudinal study with 4-year follow-up.

OBJECTIVE: To study associations of menopausal symptoms with cardiometabolic risk factors. STUDY DESIGN: A cross-sectional and longitudinal study of a representative population sample of 1393 women aged 47-55 years with a sub-sample of 298 followed for four years. The numbers of vasomotor, psychological, somatic or pain, and urogenital menopausal symptoms were ascertained at baseline through self-report. Their associations with cardiometabolic risk factors were studied using linear regression and linear mixed-effect models. Models were adjusted for age, menopausal status, body mass index, the use of hormonal preparations, education, smoking, and alcohol consumption. MAIN OUTCOME MEASURES: Cardiometabolic risk factors included total cholesterol, low-density and high-density lipoprotein cholesterol, blood pressure, glucose, triglycerides, total and android fat mass, and physical activity. RESULTS: All cholesterol and fat mass measures had modest positive associations with menopausal symptoms. The number of vasomotor symptoms, in particular, was associated with total cholesterol (B = 0.13 mmol/l, 95 % CI [0.07, 0.20]; 0.15 mmol/l [0.02, 0.28]) and low-density lipoprotein cholesterol (0.08 mmol/l [0.03, 0.14]; 0.12 mmol/l [0.01, 0.09]) in cross-sectional and longitudinal analyses, respectively. However, these associations disappeared after adjusting for confounders. The number of symptoms was not associated with blood pressure, glucose, triglycerides, and physical activity. Menopausal symptoms at baseline did not predict the changes in the risk factors during the follow-up. CONCLUSIONS: Menopausal symptoms may not be independently associated with cardiometabolic risk, and they do not seem to predict the changes in risk factors during the menopausal transition.

Body weight and premature retirement: population-based evidence from Finland.

BACKGROUND: Health status is a principal determinant of labour market participation. In this study, we examined whether excess weight is associated with withdrawal from the labour market owing to premature retirement. METHODS: The analyses were based on nationally representative data from Finland over the period 2001-15 (N ∼ 2500). The longitudinal data included objective measures of body weight (i.e. body mass index and waist circumference) linked to register-based information on actual retirement age. The association between the body weight measures and premature retirement was modelled using cubic b-splines via logistic regression. The models accounted for other possible risk factors and potential confounders, such as smoking and education. RESULTS: Excess weight was associated with an increased risk of premature retirement for both men and women. A closer examination revealed that the probability of retirement varied across the weight distribution and the results differed between sexes and weight measures. CONCLUSION: Body weight outside a recommended range elevates the risk of premature retirement.

Predicting the age at natural menopause in middle-aged women.

OBJECTIVE: To predict the age at natural menopause (ANM). METHODS: Cox models with time-dependent covariates were utilized for ANM prediction using longitudinal data from 47 to 55-year-old women (n = 279) participating in the Estrogenic Regulation of Muscle Apoptosis study. The ANM was assessed retrospectively for 105 women using bleeding diaries. The predictors were chosen from the set of 32 covariates by using the lasso regression (model 1). Another easy-to-access model (model 2) was created by using a subset of 16 self-reported covariates. The predictive performance was quantified with c-indices and by studying the means and standard deviations of absolute errors (MAE ±â€ŠSD) between the predicted and observed ANM. RESULTS: Both models included alcohol consumption, vasomotor symptoms, self-reported physical activity, and relationship status as predictors. Model 1 also included estradiol and follicle-stimulating hormone levels as well as SD of menstrual cycle length, while model 2 included smoking, education, and the use of hormonal contraception as additional predictors. The mean c-indices of 0.76 (95% CI 0.71-0.81) for model 1 and 0.70 (95% CI 0.65-0.75) for model 2 indicated good concordance between the predicted and observed values. MAEs of 0.56 ±â€Š0.49 and 0.62 ±â€Š0.54 years respectively for model 1 and 2 were clearly smaller than the MAE for predicted sample mean (1.58 ±â€Š1.02). CONCLUSIONS: In addition to sex hormone levels, irregularity of menstrual cycle, and menopausal symptoms, also life habits and socioeconomic factors may provide useful information for ANM prediction. The suggested approach could add value for clinicians' decision making related to the use of contraception and treatments for menopausal symptoms in perimenopausal women.

Video Summary:http://links.lww.com/MENO/A743 .

Adjusting for selective non-participation with re-contact data in the FINRISK 2012 survey.

AIMS: A common objective of epidemiological surveys is to provide population-level estimates of health indicators. Survey results tend to be biased under selective non-participation. One approach to bias reduction is to collect information about non-participants by contacting them again and asking them to fill in a questionnaire. This information is called re-contact data, and it allows to adjust the estimates for non-participation. METHODS: We analyse data from the FINRISK 2012 survey, where re-contact data were collected. We assume that the respondents of the re-contact survey are similar to the remaining non-participants with respect to the health given their available background information. Validity of this assumption is evaluated based on the hospitalisation data obtained through record linkage of survey data to the administrative registers. Using this assumption and multiple imputation, we estimate the prevalences of daily smoking and heavy alcohol consumption and compare them to estimates obtained with a commonly used assumption that the participants represent the entire target group. RESULTS: When adjusting for non-participation using re-contact data, higher prevalence estimates were observed compared to prevalence estimates based on participants only. Among men, the smoking prevalence estimate was 28.5% (23.2% for participants) and heavy alcohol consumption prevalence was 9.4% (6.8% for participants). Among women, smoking prevalence was 19% (16.5% for participants) and heavy alcohol consumption was 4.8% (3% for participants). CONCLUSIONS: The utilisation of re-contact data is a useful method to adjust for non-participation bias on population estimates in epidemiological surveys.

Selection bias was reduced by recontacting nonparticipants.

OBJECTIVE: One of the main goals of health examination surveys is to provide unbiased estimates of health indicators at the population level. We demonstrate how multiple imputation methods may help to reduce the selection bias if partial data on some nonparticipants are collected. STUDY DESIGN AND SETTING: In the FINRISK 2007 study, a population-based health study conducted in Finland, a random sample of 10,000 men and women aged 25-74 years were invited to participate. The study included a questionnaire data collection and a health examination. A total of 6,255 individuals participated in the study. Out of 3,745 nonparticipants, 473 returned a simplified questionnaire after a recontact. Both the participants and the nonparticipants were followed up for death and hospitalizations. The follow-up data allowed to check the assumptions on the missing data mechanism, and tailored multiple imputation methods were used to handle the missing data. RESULTS: Nonparticipation is a strong predictor for mortality in the five-year follow-up. However, the recontact response does not predict mortality or morbidity among the nonparticipants when adjusted for age and sex. The result suggests that the recontact respondents can be used as proxy for all nonparticipants. A comparison of raw estimates and estimates adjusted for selection bias reveals clear differences in the estimated population prevalences of smoking and heavy alcohol usage. CONCLUSION: All efforts to collect data on nonparticipants are likely to be useful even if the response rate for the recontact remains low. Statistical analysis of the recontact respondents provides an indication of the extent of the selection bias, even in studies where follow-up data are not available to check the assumptions.

How many longitudinal covariate measurements are needed for risk prediction?

OBJECTIVE: In epidemiologic follow-up studies, many key covariates, such as smoking, use of medication, blood pressure, and cholesterol, are time varying. Because of practical and financial limitations, time-varying covariates cannot be measured continuously, but only at certain prespecified time points. We study how the number of these longitudinal measurements can be chosen cost-efficiently by evaluating the usefulness of the measurements for risk prediction. STUDY DESIGN AND SETTING: The usefulness is addressed by measuring the improvement in model discrimination between models using different amounts of longitudinal information. We use simulated follow-up data and the data from the Finnish East-West study, a follow-up study, with eight longitudinal covariate measurements carried out between 1959 and 1999. RESULTS: In a simulation study, we show how the variability and the hazard ratio of a time-varying covariate are connected to the importance of remeasurements. In the East-West study, it is seen that for older people, the risk predictions obtained using only every other measurement are almost equivalent to the predictions obtained using all eight measurements. CONCLUSION: Decisions about the study design have significant effects on the costs. The cost-efficiency can be improved by applying the measures of model discrimination to data from previous studies and simulations.

Lifetime cumulative risk factors predict cardiovascular disease mortality in a 50-year follow-up study in Finland.

BACKGROUND: Systolic blood pressure, total cholesterol and smoking are known predictors of cardiovascular disease (CVD) mortality. Less is known about the effect of lifetime accumulation and changes of risk factors over time as predictors of CVD mortality, especially in very long follow-up studies. METHODS: Data from the Finnish cohorts of the Seven Countries Study were used. The baseline examination was in 1959 and seven re-examinations were carried out at approximately 5-year intervals. Cohorts were followed up for mortality until the end of 2011. Time-dependent Cox models with regular time-updated risk factors, time-dependent averages of risk factors and latest changes in risk factors, using smoothing splines to discover nonlinear effects, were used to analyse the predictive effect of risk factors for CVD mortality. RESULTS: A model using cumulative risk factors, modelled as the individual-level averages of several risk factor measurements over time, predicted CVD mortality better than a model using the most recent measurement information. This difference seemed to be most prominent for systolic blood pressure. U-shaped effects of the original predictors can be explained by partitioning a risk factor effect between the recent level and the change trajectory. The change in body mass index predicted the risk although body mass index itself did not. CONCLUSIONS: The lifetime accumulation of risk factors and the observed changes in risk factor levels over time are strong predictors of CVD mortality. It is important to investigate different ways of using the longitudinal risk factor measurements to take full advantage of them.

ESR1 genetic variants, haplotypes and the risk of coronary heart disease and ischemic stroke in the Finnish population: a prospective follow-up study.

Association of estrogen receptor 1 (ESR1) gene variants and risk of coronary heart disease (CHD) and ischemic stroke was evaluated in the FINRISK-study. From 14,140 individuals, 2225 were selected for genotyping using a case-cohort design. Time-to-event analysis showed that the CC genotype of -397T/C ERS1 gene contributed to higher risk of CHD only in men (HR, 1.68, CI 1.03-2.74). The -351A/G polymorphism was not independently associated with CHD. Haplotype analysis of these two variants indicated that in men, haplotype TA conferred lower risk of CHD (HR=0.72, CI 0.55-0.95), whereas men with haplotype CA had 1.8 higher risk of CHD events (CI 1.21-2.77), compared to other haplotypes. No association was found with ischemic stroke. Our study suggests that the minor allele -397C of the ESR1 gene confers risk of CHD among Finnish men, both in homozygous state and as part of a haplotype with the -351A allele.

Relative risks for stroke by age, sex, and population based on follow-up of 18 European populations in the MORGAM Project.

BACKGROUND AND PURPOSE: Within the framework of the MOnica Risk, Genetics, Archiving and Monograph (MORGAM) Project, the variations in impact of classical risk factors of stroke by population, sex, and age were analyzed. METHODS: Follow-up data were collected in 43 cohorts in 18 populations in 8 European countries surveyed for cardiovascular risk factors. In 93 695 persons aged 19 to 77 years and free of major cardiovascular disease at baseline, total observation years were 1 234 252 and the number of stroke events analyzed was 3142. Hazard ratios were calculated by Cox regression analyses. RESULTS: Each year of age increased the risk of stroke (fatal and nonfatal together) by 9% (95% CI, 9% to 10%) in men and by 10% (9% to 10%) in women. A 10-mm Hg increase in systolic blood pressure involved a similar increase in risk in men (28%; 24% to 32%) and women (25%; 20% to 29%). Smoking conferred a similar excess risk in women (104%; 78% to 133%) and in men (82%; 66% to 100%). The effect of increasing body mass index was very modest. Higher high-density lipoprotein cholesterol levels decreased the risk of stroke more in women (hazard ratio per mmol/L 0.58; 0.49 to 0.68) than in men (0.80; 0.69 to 0.92). The impact of the individual risk factors differed somewhat between countries/regions with high blood pressure being particularly important in central Europe (Poland and Lithuania). CONCLUSIONS: Age, sex, and region-specific estimates of relative risks for stroke conferred by classical risk factors in various regions of Europe are provided. From a public health perspective, an important lesson is that smoking confers a high risk for stroke across Europe.

Visualizing covariates in proportional hazards model.

We present a graphical method called the rank-hazard plot that visualizes the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0, 1]. The relative hazard is plotted with respect to the reference hazard, which can be, for example, the hazard related to the median of the covariate. Transformation to scaled ranks allows plotting of covariates measured in different units in the same graph, which helps in the interpretation of the epidemiological relevance of the covariates. Rank-hazard plots show the difference of hazards between the extremes of the covariate values present in the data and can be used as a tool to check if the proportional hazards assumption leads to reasonable estimates for individuals with extreme covariate values. Alternative covariate definitions or different transformations applied to covariates can be also compared using rank-hazard plots. We apply rank-hazard plots to the data from the FINRISK study where population-based cohorts have been followed up for events of cardiovascular diseases and compare the relative importance of the covariates cholesterol, smoking, blood pressure and body mass index. The data from the Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT) are used to visualize nonlinear covariate effects. The proposed graphics work in other regression models with different interpretations of the y-axis.