Karri Seppä

Dietary Intervention in Infancy and Later Signs of Beta-Cell Autoimmunity

BACKGROUND Early exposure to complex dietary proteins may increase the risk of beta-cell autoimmunity and type 1 diabetes in children with genetic susceptibility. We tested the hypothesis that supplementing breast milk with highly hydrolyzed milk formula would decrease the cumulative incidence of diabetes-associated autoantibodies in such children. METHODS In this double-blind, randomized trial, we assigned 230 infants with HLA-conferred susceptibility to type 1 diabetes and at least one family member with type 1 diabetes to receive either a casein hydrolysate formula or a conventional, cows-milk-based formula (control) whenever breast milk was not available during the first 6 to 8 months of life. Autoantibodies to insulin, glutamic acid decarboxylase (GAD), the insulinoma-associated 2 molecule (IA-2), and zinc transporter 8 were analyzed with the use of radiobinding assays, and islet-cell antibodies were analyzed with the use of immunofluorescence, during a median observation period of 10 years (mean, 7.5). The children were monitored for incident type 1 diabetes until they were 10 years of age. RESULTS The unadjusted hazard ratio for positivity for one or more autoantibodies in the casein hydrolysate group, as compared with the control group, was 0.54 (95% confidence interval [CI], 0.29 to 0.95), and the hazard ratio adjusted for an observed difference in the duration of exposure to the study formula was 0.51 (95% CI, 0.28 to 0.91). The unadjusted hazard ratio for positivity for two or more autoantibodies was 0.52 (95% CI, 0.21 to 1.17), and the adjusted hazard ratio was 0.47 (95% CI, 0.19 to 1.07). The rate of reported adverse events was similar in the two groups. CONCLUSIONS Dietary intervention during infancy appears to have a long-lasting effect on markers of beta-cell autoimmunity--markers that may reflect an autoimmune process leading to type 1 diabetes. (ClinicalTrials.gov number, NCT00570102.).

Choosing the relative survival method for cancer survival estimation

BACKGROUND The methods on how to calculate cumulative relative survival have been ambiguous and have given differences in empirical results. METHODS The gold standard for the cumulative relative survival ratio is the weighted average of age-specific cumulative relative survival ratios, with weights proportional to numbers of patients at diagnosis. Mathematics and representative empirical materials from the population-based Finnish Cancer Registry were studied for the different relative survival methods and compared with the gold standard. RESULTS The theoretical and empirical results show a good agreement between the method suggested in 1959 by Ederer and Heise (the so-called Ederer II method) and the gold standard. This result is in part due the fact that as follow-up time increases the conditional (annual) relative survival ratios become increasingly more independent of age. Moreover, the dependence between the excess mortality due to cancer and the baseline general mortality does not introduce an important enough selection in practice to cause a notable bias. CONCLUSION The use of the method by Ederer and Heise, multiplication of the annual relative survival ratios, instead of direct standardisation, should be considered in future applications. This would be particularly important for the long-term follow-up when age-specific relative survival is not available in the oldest age categories.

Cure fraction model with random effects for regional variation in cancer survival.

Assessing regional differences in the survival of cancer patients is important but difficult when separate regions are small or sparsely populated. In this paper, we apply a mixture cure fraction model with random effects to cause-specific survival data of female breast cancer patients collected by the population-based Finnish Cancer Registry. Two sets of random effects were used to capture the regional variation in the cure fraction and in the survival of the non-cured patients, respectively. This hierarchical model was implemented in a Bayesian framework using a Metropolis-within-Gibbs algorithm. To avoid poor mixing of the Markov chain, when the variance of either set of random effects was close to zero, posterior simulations were based on a parameter-expanded model with tailor-made proposal distributions in Metropolis steps. The random effects allowed the fitting of the cure fraction model to the sparse regional data and the estimation of the regional variation in 10-year cause-specific breast cancer survival with a parsimonious number of parameters. Before 1986, the capital of Finland clearly stood out from the rest, but since then all the 21 hospital districts have achieved approximately the same level of survival.

Mean and median survival times of cancer patients should be corrected for informative censoring

OBJECTIVE To estimate unbiasedly the mean and median survival time of cancer patients with incomplete follow-up. To assess the effects of informative censoring on estimates by using the crude and a bias-reducing method, based on weighted averages of the age-specific results. STUDY DESIGN AND SETTING Colon cancer patients diagnosed in 1970-1979 and thyroid cancer patients diagnosed in 1978-1987 were followed up until the end of each diagnosis period. RESULTS Because of informative censoring, the crude estimates of the mean lifetime grossly overestimate the survival of the colon cancer patients and underestimate the survival of the thyroid cancer patients. Together with the most recent population life tables, the bias-reducing method succeeds in estimating the mean and the median lifetime accurately. CONCLUSION Stratifying by age is essential when the mean or median lifetime of the patients with a wide age range is to be estimated. The bias-reducing method should be used if a single summary estimate for the whole patient group is needed. The median is preferable if more than half of the patients die soon after diagnosis. Predicted population life tables should be used in extrapolation.

Comparing net survival estimators of cancer patients

The net survival of a patient diagnosed with a given disease is a quantity often interpreted as the hypothetical survival probability in the absence of causes of death other than the disease. In a relative survival framework, net survival summarises the excess mortality that patients experience compared with their relevant reference population. Based on follow-up data from the Finnish Cancer Registry, we derived simulation scenarios that describe survival of patients in eight cancer sites reflecting different excess mortality patterns in order to compare the performance of the classical Ederer II estimator and the new estimator proposed by Pohar Perme et al. At 5 years, the age-standardised Ederer II estimator performed equally well as the Pohar Perme estimator with the exception of melanoma in which the Pohar Perme estimator had a smaller mean squared error (MSE). At 10 and 15 years, the age-standardised Ederer II performed most often better than the Pohar Perme estimator. The unstandardised Ederer II estimator had the largest MSE at 5 years. However, its MSE was often superior to those of the other estimators at 10 and 15 years, especially in sparse data. Both the Pohar Perme and the age-standardised Ederer II estimator are valid for 5-year net survival of cancer patients. For longer-term net survival, our simulation results support the use of the age-standardised Ederer II estimator.

Overexpression of hypoxia-inducible factor 1 alpha impacts FoxP3 levels in mycosis fungoides-Cutaneous T-cell lymphoma: Clinical implications

Mycosis fungoides (MF) is the most common variant of primary cutaneous T‐cell lymphoma, and decreased forkhead box P3 (FoxP3) expression has been reported in MF late stages. Hypoxia‐inducible factor 1 alpha (HIF‐1α) may regulate FoxP3 expression; however, it is unknown whether HIF‐1α is expressed in the CD4+ T cells of MF patients and how it could affect the expression of FoxP3. Therefore, we evaluated the expression of HIF‐1α and FoxP3 in CD4+ T cells obtained from the skin lesions of MF patients. We found increased cell proliferation and an increase in CD4+ T cells with an aberrant phenotype among early stage MF patients. HIF‐1α was overexpressed in these CD4+ T cells. In addition, we found a decrease in the percentage of FoxP3+ cells both in the skin of MF patients, when compared with control skin samples, and with disease progression. In addition, a negative correlation was established between HIF‐1α and FoxP3 expression. Skin HIF‐1α expression in MF patients correlated with the extent of the affected area and increased with the disease progression. Finally, we showed that ex vivo inhibition of HIF‐1α degradation increases the percentage of FoxP3+ T cells in skin lesions. Our results suggest that overexpression of HIF‐1α affects the levels of FoxP3 in MF patients, which could have relevant implications in terms of disease outcome.

Chernobyl fallout and cancer incidence in Finland.

Twenty‐five years have passed since the Chernobyl accident, but its health consequences remain to be well established. Finland was one of the most heavily affected countries by the radioactive fallout outside the former Soviet Union. We analyzed the relation of the estimated external radiation exposure from the fallout to cancer incidence in Finland in 1988–2007. The study cohort comprised all ∼3.8 million Finns who had lived in the same dwelling for 12 months following the accident (May 1986–April 1987). Radiation exposure was estimated using data from an extensive mobile dose rate survey. Cancer incidence data were obtained for the cohort divided into four exposure categories (the lowest with the first‐year committed dose <0.1 mSv and the highest ≥0.5 mSv) allowing for a latency of 5 years for leukemia and thyroid cancer, and 10 years for other cancers. Of the eight predefined cancer sites regarded as radiation‐related from earlier studies, only colon cancer among women showed an association with exposure from fallout [excess rate ratio per increment in exposure category 0.06, 95% confidence interval (CI) 0.02–0.11]. No such effect was observed for men, or other cancer sites. Our analysis of a large cohort over two decades did not reveal an increase in cancer incidence following the Chernobyl accident, with the possible exception of colon cancer among women. The largely null findings are consistent with extrapolation from previous studies suggesting that the effect is likely to remain too small to be empirically detectable and of little public health impact.

Chernobyl fallout and cancer incidence in Finland 1988-2007

Twenty‐five years have passed since the Chernobyl accident, but its health consequences remain to be well established. Finland was one of the most heavily affected countries by the radioactive fallout outside the former Soviet Union. We analyzed the relation of the estimated external radiation exposure from the fallout to cancer incidence in Finland in 1988–2007. The study cohort comprised all ∼3.8 million Finns who had lived in the same dwelling for 12 months following the accident (May 1986–April 1987). Radiation exposure was estimated using data from an extensive mobile dose rate survey. Cancer incidence data were obtained for the cohort divided into four exposure categories (the lowest with the first‐year committed dose <0.1 mSv and the highest ≥0.5 mSv) allowing for a latency of 5 years for leukemia and thyroid cancer, and 10 years for other cancers. Of the eight predefined cancer sites regarded as radiation‐related from earlier studies, only colon cancer among women showed an association with exposure from fallout [excess rate ratio per increment in exposure category 0.06, 95% confidence interval (CI) 0.02–0.11]. No such effect was observed for men, or other cancer sites. Our analysis of a large cohort over two decades did not reveal an increase in cancer incidence following the Chernobyl accident, with the possible exception of colon cancer among women. The largely null findings are consistent with extrapolation from previous studies suggesting that the effect is likely to remain too small to be empirically detectable and of little public health impact.

Estimating multilevel regional variation in excess mortality of cancer patients using integrated nested Laplace approximation: Regional variation in excess mortality of cancer patients

Models of excess mortality with random effects were used to estimate regional variation in relative or net survival of cancer patients. Statistical inference for these models based on the Markov chain Monte Carlo (MCMC) methods is computationally intensive and, therefore, not feasible for routine analyses of cancer register data. This study assessed the performance of the integrated nested Laplace approximation (INLA) in monitoring regional variation in cancer survival. Poisson regression model of excess mortality including both spatially correlated and unstructured random effects was fitted to the data of patients diagnosed with ovarian and breast cancer in Finland during 1955-2014 with follow up from 1960 through 2014 by using the period approach with five-year calendar time windows. We estimated standard deviations associated with variation (i) between hospital districts and (ii) between municipalities within hospital districts. Posterior estimates based on the INLA approach were compared to those based on the MCMC simulation. The estimates of the variation parameters were similar between the two approaches. Variation within hospital districts dominated in the total variation between municipalities. In 2000-2014, the proportion of the average variation within hospital districts was 68% (95% posterior interval: 35%-93%) and 82% (60%-98%) out of the total variation in ovarian and breast cancer, respectively. In the estimation of regional variation, the INLA approach was accurate, fast, and easy to implement by using the R-INLA package.

Glioblastoma survival is improving despite increasing incidence rates: a nationwide study between 2000 and 2013 in Finland

BACKGROUND We assessed population-level changes in glioblastoma survival between 2000 and 2013 in Finland, with focus on elderly patients (>70 y) in order to assess if changes in treatment of glioblastoma are reflected also in population-based survival rates. METHODS We identified all patients (age ≥18 y) from the Finnish Cancer Registry (FCR) with a histopathological diagnosis of primary glioblastoma in 2000-2013. Patients were followed up until December 2015. The accuracy of register-based search of glioblastoma patients was internally validated. We report age-standardized relative survival ratios and relative excess risks (RERs) of death in 2000-2006 (pre-period) and 2007-2013 (post-period). RESULTS We identified 2045 glioblastoma patients from the FCR. The accuracy of the FCR-based search was 97%. Median age was 63.3 years, and 42% were women. Incidence increased on average by 1.6% (P = 0.004) and median age by 0.4 years per calendar year. Between the pre- and post-periods, the proportion of patients >70 years increased from 24% to 27%. In >70-year-old patients, the median survival time increased from 3.6 months in 2000-2006 to 4.5 months in 2007-2013 (RER 0.82, 95% CI: 0.68-0.98). In ≤70-year-old patients, the median survival time increased from 9.3 months in 2000-2006 to 11.7 months in 2007-2013 (RER 0.74, 95% CI: 0.67-0.82). CONCLUSION Despite the increased proportion of elderly glioblastoma patients, population-level survival of glioblastoma patients has improved since the year 2000. However, increasing incidence, increasing age of patients, and poor survival in elderly are alarming, and future studies should perhaps focus more on elderly.