Grandfathers Journal Part 2

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Stetson, who has fond memories of picking blueberrries at home on P. He had to live with those haunting memories until his death at age 98 in But it was his only outlet, and I wish when the teenage years came, I listened and held on to those stories. Top News. The driving force behind success. Should I stay or should I go? A look at graduate retention. A conversation with Canadian Armed Forces veteran and health Generational value gaps shifting as individualist thinking warps view Success: Two women. Two lives.

One take. Five questions, 10 answers: let's make prejudice, inequality history. How do we define success? Sorry you must be at least 19 years of age to consume this content. MacArthur was a stretcher-bearer in the thick of the war. C Company chorus, directed by Christine Schultz, singing the marching song of the th P.

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Regiment during the commemorative event held at Trinity United Church on Sunday afternoon. More Local stories. Greens introduce bill to pushing for higher emissions reduction targets Published 9 hours ago. Cardiovascular mortality on the other hand was associated with parental, but not grandparental, nutritional experience. Their findings have been discussed in renowned peer-reviewed journals 20 , 21 , 22 , 23 , 24 and are cited over 2, times October To establish evidence for a transgenerational response to early human nutritional experience, carried via the germline rather than based on direct in-utero exposure or maintained through continuity in social circumstances, we need to observe at least three generations and exogenous variation in the early nutritional conditions of the first generation.

In addition, we need intergenerational social data. Firstly, food access for six ancestors was examined in three small birth cohorts, where family ties were interwoven and rather complex. This has given rise to the concern that a multitude of comparisons have produced random or biased findings 25 , Secondly, since crop failures and very abundant harvests were rather infrequent, exposure to a nutritional shock during pre-puberty implies that individuals were born in particular years, leaving room for confounding due to birth cohort effects. In this we are inspired by the work of Frankel et al.

Easy or difficult access to food in a certain year and region is measured from this statistic. The validity of this method is discussed in the Methods section. Our results support the hypothesis that a transgenerational response to abundant food in pre-puberty exists, along the male but not along the female line.

We estimated the effects of food access in generation 0 G0 on mortality in their grandchildren G2 and in their children G1. All results refer to G0 food access during the slow growth period, defined as ages 9—12 for boys and 8—10 for girls 9. Thus, our results suggest an elevated mortality among grandsons of paternal grandfathers with good access to food.

This association should therefore be interpreted with caution. This excess mortality was found both in tobacco-related cancers and cancers not related to tobacco. Thus, cancer deaths appear to drive the all-cause mortality result for paternal grandfathers in our study. For paternal grandfathers with good access to food during their SGP, we find an elevated mortality in grandsons but not in granddaughters.

This excess risk among male grandchildren is also highlighted in Pembrey et al. However, we were unable to reproduce any of their findings for cardiovascular diseases and diabetes. However, based on deaths, we did find that male offspring of fathers with good access to food were more prone to diabetes. A key finding of our study concerns cancer, a common cause-of-death in the grandchild generation. Following Frankel et al. Both categories showed an elevated mortality among grandsons of paternal grandfathers with good access to food; among granddaughters cancer mortality was not elevated at all.

This gender interaction was driven by cancers not related to tobacco. The use of an alternative classification of cancers 28 gave the same results and served as a sensitivity test. Some differences between the two studies should be considered. Mostly, only the results after full control of all ancestors were reported.

We were only able to take three ancestors into account all in the paternal or maternal line. Controlling for family social circumstances was done in both studies as a way to rule out the possibility that the observed associations were driven by social or cultural factors. It is also important to take secular trends in the use of fertilizers and pesticides into consideration. During the period, such practices were still on a low level When one looks at all significant estimates, they are without exception stronger in models 2 controlling for G0 birth year than in models 1.

This suggests that our results are not due to confounding from secular trends in smoking or farming practices. The above analysis comprises multiple comparisons. That this may introduce random results is a legitimate concern. There are four grandparents, each exposed to two events poor or good harvest , with one outcome all-cause mortality in the two genders in G2 plus two outcomes diabetes and CVD mortality in the two genders combined.

In other words, we replicate 32 analyses in G2. The chance that both will then point in the same direction significantly high or significantly low is 0. In the 24 G1 analyses it is 0. Since the one result that we did reproduce, involving paternal grandfathers, is the one with the strongest a priori backing 11 , 19 , our parallel findings are unlikely to be due to chance.

The discrepancies between our two studies concern diabetes and CVD and could be due to randomness or to differences in real-life contexts of the two studies, such as the recording of cause-of-death. Since our population of grandchildren was born in a more recent era, some historical effects may simply not be revealed today. Early death due to infections became more unusual throughout the 20th century; cardiovascular disease in Sweden has been declining since around , while cancer is slowly becoming a more common cause-of-death. The conclusion in Pembrey et al. CVD played no role in this excess mortality among G2 men in either study.

Neither did diabetes in our study. We suggest that the excess mortality among male grandchildren whose paternal grandfathers enjoyed good access to food, found in both studies, is at least partly based on cancer mortality. Alternatively, the mortality excess among grandsons could also be of a more general kind, reflecting general susceptibility transmitted across generations. The finding of a transgenerational response to abundant childhood nutrition in both studies does not prove that the pathway is epigenetic.

We refer broadly to epigenetics as heritable changes of gene function not induced by changes in the DNA sequence. Such changes can nevertheless be influenced by DNA sequence. Epigenetic events such as methylation of DNA could be genotype-dependent, as shown by several authors, for instance 30 , When this is the case we will only be able to observe an average effect, across genotypes.

De novo mutations in the grandparental generation could happen as a response to specific exposures, such as fertilizers, pesticides and mold. Regional differences in harvests could in principle be influenced by differences in the use of fertilizers and pesticides. However, this practice hardly seems common enough at this time 29 to cause new mutations on a large scale. It might have been more difficult to store food during rich harvests when all available storing capacity was exploited.

There is a lack of data concerning these potential exposures in the period and we can only speculate about their role. Previous studies of intergenerational effects on offspring health or survival have often focused on starvation or severe food shortage 33 , This focus may have been methodologically rewarding but theoretically narrow.

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Nutritional signals may be based on subtle differences in amount and kind of nutrition. Abundant harvests should mean good access to grains and many vegetables, rich in folates and working as methyl donors. Waterland and Jirtle 35 discuss the importance of dietary methyl donors for DNA methylation. The reference to epigenetic aging suggests a vulnerability to disease in general.


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Two elements in the genome may be especially sensitive to nutritional dysregulation: transposons and imprinted genes Epigenetics play a central role in neoplasia 36 , 37 , Numerous small ncRNAs are exclusively or preferentially expressed in testis or germ cells in humans and mice Reddy 40 concluded that aberrant miRNA expression is a rule rather than an exception in carcinogenesis. Hypermethylation of CpG islands upstream from tumor suppressor genes would influence cancer risk The silencing of tumor suppressor miRNAs contributes to the development of human cancer and metastasis A transgenerational response to abundant childhood food could thus be based either on epigenetic mechanisms linked to nutrition, or de novo mutations linked to new farming practices, or both together since these mechanisms are not mutually exclusive.

Frankel et al. Controlling for social factors they observed that boys and girls consuming rich food highest fifth of energy content were more than twice as likely to die of cancer later in life compared to the lowest fifth. This effect was particularly strong for cancer not related to tobacco. In their view, it was the high energy content of the food that gave rise to this effect rather than any mutagenic or carcinogenic substances in food. Still, assuming that the number of cells in organs of the body respond to abundant nutrition early in life, the risk of de novo mutations due to chance would increase in proportion to cell numbers.

Consistent with this hypothesis, obesity 43 , body height 44 and number of stem cells in specific organs 45 , 46 have all been linked to cancer mortality. Abundance of food, when the body is just about to leap into puberty, i. Soubry et al. One of them is the period just before puberty, equivalent to the slow growth period, which is the period we have examined in this replication study.

Could nutritional experience in one generation thus trigger a transgenerational response in subsequent generations? We would like to be cautious about the specific mechanism. However, the hypothesis that a molecular signal of abundant nutrition received in this period could be captured by male gametes, cannot be rejected and should thus be further explored 8.

The implications for our understanding of ourselves are substantial Its multigenerational extension includes the 12, individuals in the first generation G1 who have later been identified by their personal ID number. We traced their now deceased parents G0 manually and their children G2 through linking to the Swedish Multigenerational Registry. This means that we usually have information about paternal or maternal grandparents, but not about all four. The multigenerational data base is suitable for our purpose 48 , Three linked generations. Birth year distribution of G0, G1 and G2 by gender number of individuals born each year; left Y -axis and annual average harvest quality for years — right Y -axis , which period corresponds to G0 slow growth periods.

Information about G0 was obtained from the original birth records of G1 and from information collected from parish and hospital registers. Their places of birth were collected from the sixth edition of the Swedish Death Index, published by Statistics Sweden and the Swedish Genealogical Society. G0 consists of 15, individuals, born —, the overwhelming majority born outside the city of Uppsala. Our main study focus is the grandchildren G2 of these individuals.

Since we restricted the analyses to G2 individuals for whom we had harvest information for both maternal or both paternal grandparents, we were able to analyze outcomes in 11, grandchildren. Linking of individuals to patient and mortality data was made by Statistics Sweden and all analyses were made on data anonymized to researchers. This follows standard practice in large registry-based observational studies in Sweden.

Harvest variations between years and regions across Sweden were substantial.

Introduction

Exposure in G0 access to food was defined from this variation. In the year , the population of Sweden was 4. There would be a certain variation in harvests within all regions. Trade within regions would have reduced the importance of these differences, as administrative divisions often followed trade and commercial patterns.

This would certainly have reduced the within-region differences in food access. The regional average in a certain year would in all likelihood be an underestimation of actual food consumption in rich families and an overestimation in poor families. We controlled for social factors as far as possible in all analyses, which would have reduced any social bias. Finally, excluding city dwellers gave stronger estimates of effect, suggesting that actual food consumption was better captured among rural inhabitants.

Our measure of access to food was based on an annually published regional harvest statistic for — 51 ranging from 0 total crop failure to 10 abundant harvest. A weighting procedure was applied, based on the assumption that harvests took place on September 1 st. Moderate harvests were thus defined as 5—8. No G2 individual in the study population had a grandparent who experienced both good and poor harvests in SGP.

To control for parental G1 food access in analyses of the effects of grandparental G0 food access on G2 mortality we had to rely on annually published national harvest statistics, thereby losing precision. A more recent period — also meant less dramatic variation in exposure. This statistic ranged from 1 poor to 5 good , with 3 being average harvest.

We applied the same weighting procedure as for G0, and generated a binary variable for experiencing good harvest in SGP a weighted score of more than 3. Region of residence at G0 birth was known for all included G0, and used as a proxy for residence during SGP. A proportion of G0 moved between birth and age ten.

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G2 mortality data for — was obtained from the Swedish Cause-of-Death Register. CVD, diabetes or cancer as underlying or contributing cause of death were used when analyzing cause-specific mortality. In additional analyses of G2 outcomes, we also used data from the Swedish In-patient Register — An event was defined as a first case of hospitalization, or a death. Since several disease entities may be entered on a death certificate or on a hospital discharge note, individuals may contribute events to more than one cause-specific analysis.

Our analyses of all-cause mortality include two individuals with known death date but unknown cause-of-death. In model 2 we include further controls. We considered food access of other ancestors. Our grandchild mortality analysis could take into account the food access of three ancestors simultaneously—all three on either the paternal or the maternal side.

Some excerpts from my grandfather's journals - Part 2, the 50s : nosleep

There were virtually no differences between the two. Social and demographic confounders were considered. G2 year of birth was grouped into five-year age bands as a categorical variable in all tables, in model 1 and 2. Model 1 controlled for a number of social variables. G0 birth year and common ancestors were also considered. Thus, models 2 controlled for G0 birth years as linear trends and cluster standard errors at family level, to account for the fact that siblings and cousins share biological ancestors.

In analyses of G1 mortality we controlled for G1 birth year 5-year groups , G1 family social class six groups and marital status at birth plus sibling position defined as in G2 analyses. In all G1 and G2 mortality analyses, we compared G0 individuals who differed with respect to SGP ancestral food access, controlling for the above confounders in regression analyses. Hazard ratios HR were estimated by Cox proportional hazard models with age as underlying time scale. All models accounted for censoring. Left censoring arose, because our G2 data do not include individuals who died before 1 January For G1 mortality follow-up starts at 1 January earliest data with digitalized cause-of-death data.

Right censoring arose if individuals emigrated or survived beyond 31 December All analyses were performed using Stata The data that support these findings are available on reasonable request to the corresponding author [DV]. The availability of social, patient and mortality data is subject to restrictions imposed by the National Board of Health and Welfare and Statistics Sweden, in accordance with Swedish legislation on privacy protection, meaning that data can only be accessed and analyzed at a special venue in Stockholm.

A Reporting Summary is available as a supplementary information file. Weigel, D. Epialleles in plant evolution.

Grandfather's note

Genome Biol. Lim, J. Bridging the transgenerational gap with epigenetic memory. Trends Genet. Daxinger, L. Understanding transgenerational epigenetic inheritance via the gametes in mammals. Anway, M. Epigenetic transgenerational actions of endocrine disruptors and male fertility.

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Science , — Cropley, J. The penetrance of an epigenetic trait in mice is progressively yet reversibly increased by selection and environment. Williams, S. Core concepts: epigenetics.