Background: Although human longevity tends to cluster within families, genetic studies on longevity have had limited success in identifying longevity loci. One of the main causes of this limited... Show moreBackground: Although human longevity tends to cluster within families, genetic studies on longevity have had limited success in identifying longevity loci. One of the main causes of this limited success is the selection of participants. Studies generally include sporadically long-lived individuals, i.e. individuals with the longevity phenotype but without a genetic predisposition for longevity. The inclusion of these individuals causes phenotype heterogeneity which results in power reduction and bias. A way to avoid sporadically long-lived individuals and reduce sample heterogeneity is to include family history of longevity as selection criterion using a longevity family score. A main challenge when developing family scores are the large differences in family size, because of real differences in sibship sizes or because of missing data.Methods: We discussed the statistical properties of two existing longevity family scores: the Family Longevity Selection Score (FLoSS) and the Longevity Relatives Count (LRC) score and we evaluated their performance dealing with differential family size. We proposed a new longevity family score, the mLRC score, an extension of the LRC based on random effects modeling, which is robust for family size and missing values. The performance of the new mLRC as selection tool was evaluated in an intensive simulation study and illustrated in a large real dataset, the Historical Sample of the Netherlands (HSN).Results: Empirical scores such as the FLOSS and LRC cannot properly deal with differential family size and missing data. Our simulation study showed that mLRC is not affected by family size and provides more accurate selections of long-lived families. The analysis of 1105 sibships of the Historical Sample of the Netherlands showed that the selection of long-lived individuals based on the mLRC score predicts excess survival in the validation set better than the selection based on the LRC score .Conclusions: Model-based score systems such as the mLRC score help to reduce heterogeneity in the selection of long-lived families. The power of future studies into the genetics of longevity can likely be improved and their bias reduced, by selecting long-lived cases using the mLRC. Show less
Mourits, R.J.; Berg, N. van den; Rodriguez-Girondo, M.; Mandemakers, K.; Slagboom, P.E.; Beekman, M.; Janssens, A.A.P.O. 2020
Studies have shown that long-lived individuals seem to pass their survival advantage on to their offspring. Offspring of long-lived parents had a lifelong survival advantage over individuals... Show moreStudies have shown that long-lived individuals seem to pass their survival advantage on to their offspring. Offspring of long-lived parents had a lifelong survival advantage over individuals without long-lived parents, making them more likely to become long-lived themselves. We test whether the survival advantage enjoyed by offspring of long-lived individuals is explained by environmental factors. 101,577 individuals from 16,905 families in the 1812-1886 Zeeland cohort were followed over time. To prevent that certain families were overrepresented in our data, disjoint family trees were selected. Offspring was included if the age at death of both parents was known. Our analyses show that multiple familial resources are associated with survival within the first 5 years of life, with stronger maternal than paternal effects. However, between ages 5 and 100 both parents contribute equally to offspring's survival chances. After age 5, offspring of long-lived fathers and long-lived mothers had a 16-19% lower chance of dying at any given point in time than individuals without long-lived parents. This survival advantage is most likely genetic in nature, as it could not be explained by other, tested familial resources and is transmitted equally by fathers and mothers. Show less
Berg, N. van den; Rodriguez-Girondo, M.; Mandemakers, K.; Janssens, A.A.P.O.; Beekman, M.; Slagboom, P.E. 2020
Loci associated with longevity are likely to harbor genes coding for key players of molecular pathways involved in a lifelong decreased mortality and decreased/compressed morbidity. However,... Show moreLoci associated with longevity are likely to harbor genes coding for key players of molecular pathways involved in a lifelong decreased mortality and decreased/compressed morbidity. However, identifying such loci is challenging. One of the most plausible reasons is the uncertainty in defining long-lived cases with the heritable longevity trait among long-living phenocopies. To avoid phenocopies, family selection scores have been constructed, but these have not yet been adopted as state of the art in longevity research. Here, we aim to identify individuals with the heritable longevity trait by using current insights and a novel family score based on these insights. We use a unique dataset connecting living study participants to their deceased ancestors covering 37,825 persons from 1,326 five-generational families, living between 1788 and 2019. Our main finding suggests that longevity is transmitted for at least two subsequent generations only when at least 20% of all relatives are long-lived. This proves the importance of family data to avoid phenocopies in genetic studies. Show less
Boer, A. de; Harteveld, A.A.; Stemkens, B.; Blankestijn, P.J.; Bos, C.; Franklin, S.L.; ... ; Leiner, T. 2020
Background: Renal multiparametric magnetic resonance imaging (MRI) is a promising tool for diagnosis, prognosis, and treatment monitoring in kidney disease.Purpose: To determine intrasubject test... Show moreBackground: Renal multiparametric magnetic resonance imaging (MRI) is a promising tool for diagnosis, prognosis, and treatment monitoring in kidney disease.Purpose: To determine intrasubject test-retest repeatability of renal MRI measurements.Study Type: Prospective.Population: Nineteen healthy subjects aged over 40 years.Field Strength/Sequences: T-1 and T-2 mapping, R-2* mapping or blood oxygenation level-dependent (BOLD) MRI, diffusion tensor imaging (DTI), and intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI), 2D phase contrast, arterial spin labelling (ASL), dynamic contrast enhanced (DCE) MRI, and quantitative Dixon for fat quantification at 3T.Assessment: Subjects were scanned twice with similar to 1 week between visits. Total scan time was similar to 1 hour. Postprocessing included motion correction, semiautomated segmentation of cortex and medulla, and fitting of the appropriate signal model. Statistical Test: To assess the repeatability, a Bland-Altman analysis was performed and coefficients of variation (CoVs), repeatability coefficients, and intraclass correlation coefficients were calculated.Results: CoVs for relaxometry (T-1, T-2, R-2*/BOLD) were below 6.1%, with the lowest CoVs for T-2 maps and highest for R-2*/BOLD. CoVs for all diffusion analyses were below 7.2%, except for perfusion fraction (FP), with CoVs ranging from 18-24%. The CoV for renal sinus fat volume and percentage were both around 9%. Perfusion measurements were most repeatable with ASL (cortical perfusion only) and 2D phase contrast with CoVs of 10% and 13%, respectively. DCE perfusion had a CoV of 16%, while single kidney glomerular filtration rate (GFR) had a CoV of 13%. Repeatability coefficients (RCs) ranged from 7.7-87% (lowest/highest values for medullary mean diffusivity and cortical FP, respectively) and intraclass correlation coefficients (ICCs) ranged from -0.01 to 0.98 (lowest/highest values for cortical FP and renal sinus fat volume, respectively).Data Conclusion: CoVs of most MRI measures of renal function and structure (with the exception of FP and perfusion as measured by DCE) were below 13%, which is comparable to standard clinical tests in nephrology. Show less
Berg, N. van den; Dijk, I.K. van; Mourits, R.J.; Slagboom, P.E.; Janssens, A.A.P.O.; Mandemakers, K. 2020
It remains unknown how different types of sources affect the reconstruction of life courses and families in large-scale databases increasingly common in demographic research. Here, we compare... Show moreIt remains unknown how different types of sources affect the reconstruction of life courses and families in large-scale databases increasingly common in demographic research. Here, we compare family and life-course reconstructions for 495 individuals simultaneously present in two well-known Dutch data sets: LINKS, based on the Zeeland province's full-population vital event registration data (passive registration), and the Historical Sample of the Netherlands (HSN), based on a national sample of birth certificates, with follow-up of individuals in population registers (active registration). We compare indicators of fertility, marriage, mortality, and occupational status, and conclude that reconstructions in the HSN and LINKS reflect each other well: LINKS provides more complete information on siblings and parents, whereas the HSN provides more complete life-course information. We conclude that life-course and family reconstructions based on linked passive registration of individuals constitute a reliable alternative to reconstructions based on active registration, if case selection is carefully considered. Show less
Berg, N. van den; Rodriguez-Girondo, M.; Dijk, I.K. van; Mourits, R.J.; Mandemakers, K.; Janssens, A.A.P.O.; ... ; Slagboom, P.E. 2019
