Predicting longer-term mortality risk requires collection of clinical data, which is often cumbersome. Therefore, we use a well-standardized metabolomics platform to identify metabolic predictors of ...long-term mortality in the circulation of 44,168 individuals (age at baseline 18-109), of whom 5512 died during follow-up. We apply a stepwise (forward-backward) procedure based on meta-analysis results and identify 14 circulating biomarkers independently associating with all-cause mortality. Overall, these associations are similar in men and women and across different age strata. We subsequently show that the prediction accuracy of 5- and 10-year mortality based on a model containing the identified biomarkers and sex (C-statistic = 0.837 and 0.830, respectively) is better than that of a model containing conventional risk factors for mortality (C-statistic = 0.772 and 0.790, respectively). The use of the identified metabolic profile as a predictor of mortality or surrogate endpoint in clinical studies needs further investigation.
Aging triggers intricate physiological changes, particularly in whole-body fat-free mass (FFM) and handgrip strength, affecting overall health and independence. Despite existing research, the broader ...significance of how muscle health is affected by the intricate interplay of lifestyle factors simultaneously during aging needs more exploration. This study aims to examine how nutrition, physical activity, and sleep impact on FFM and handgrip strength in middle-aged men and women, facilitating future personalized recommendations for preserving muscle health.
The cross-sectional analysis of the UK Biobank involved 45,984 individuals (54 % women) aged 40–70 years with a complete dataset. Multiple linear regression explored determinants of FFM and handgrip strength, considering traditional, socio-demographics, medication use and smoking as covariates, with sex and age (younger and older than 55 years) stratifications.
In older men and women, higher physical activity beneficially affect both FFM (respectively Β = 3.36 × 10−3, p-value = 1.66 × 10−3; Β = 2.52 × 10−3, p-value = 3.57 × 10−4) and handgrip strength (Β = 6.05 × 10−3, p-value = 7.99 × 10−5, Β = 8.98 × 10−3, p-value = 2.95 × 10−15). Similar results were found in fiber intake for FFM in older men and women (respectively B = 3.00 × 10−2, p-value = 2.76 × 10−5; B = 2.68 × 10−2, p-value = 1.78 × 10−9) and handgrip strength (Β = 3.27 × 10−2, p-value = 1.40 × 10−3; Β = 3.12 × 10−2, p-value = 1.34 × 10−5). Other lifestyle factors influence FFM and handgrip strength differently. Key determinants influencing handgrip strength included higher protein intake, lower water intake, higher alcohol intake, and extended sleep duration whereas mainly higher water intake is associated with higher FFM.
In both men and women, the main factors associated with FFM and handgrip strength are physical activity and fiber intake, which may underlie a connection between gut and muscle health. Given the observed complexity of muscle health in the age and sex strata, further longitudinal research is needed to provide personalized lifestyle recommendations.
•Lifestyle factors are associated differently in middle-aged men and women.•Physical activity and fiber intake associated with higher handgrip strength and FFM•Protein intake reveals only associated with handgrip strength in men.•Low-to-moderate alcohol intake associated with higher handgrip strength
Human lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25–30% and expected to be polygenic. Two complementary fields go hand in hand in order to ...unravel the mechanisms of biological aging: genomic and biomarker research. Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation. The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next‐generation sequencing. Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data – generated using novel technologies – in a wealth of studies in human populations. Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation.
Editor's suggested further reading in BioEssays: On the cause of aging and control of lifespan
The free‐radical theory of ageing – older, wiser and still alive
Longevity and the long arm of epigenetics: Acquired parental marks influence lifespan across several generations
The mystery of C. elegans aging: An emerging role for fat
A review on the accomplishments of genetic, transcriptomic, and epigenetic studies into the biology of aging and longevity by molecular and epidemiological analysis of human populations using family‐ and population‐based study designs. In addition, the development of novel biomarkers of aging and their use for integrated data analysis is discussed.
Telomere length (TL) has been associated with aging and mortality, but individual differences are also influenced by genetic factors, with previous studies reporting heritability estimates ranging ...from 34 to 82%. Here we investigate the heritability, mode of inheritance and the influence of parental age at birth on TL in six large, independent cohort studies with a total of 19,713 participants. The meta-analysis estimate of TL heritability was 0.70 (95% CI 0.64-0.76) and is based on a pattern of results that is highly similar for twins and other family members. We observed a stronger mother-offspring (r=0.42; P-value=3.60 × 10(-61)) than father-offspring correlation (r=0.33; P-value=7.01 × 10(-5)), and a significant positive association with paternal age at offspring birth (β=0.005; P-value=7.01 × 10(-5)). Interestingly, a significant and quite substantial correlation in TL between spouses (r=0.25; P-value=2.82 × 10(-30)) was seen, which appeared stronger in older spouse pairs (mean age ≥55 years; r=0.31; P-value=4.27 × 10(-23)) than in younger pairs (mean age<55 years; r=0.20; P-value=3.24 × 10(-10)). In summary, we find a high and very consistent heritability estimate for TL, evidence for a maternal inheritance component and a positive association with paternal age.
Survival to extreme ages clusters within families. However, identifying genetic loci conferring longevity and low morbidity in such longevous families is challenging. There is debate concerning the ...survival percentile that best isolates the genetic component in longevity. Here, we use three-generational mortality data from two large datasets, UPDB (US) and LINKS (Netherlands). We study 20,360 unselected families containing index persons, their parents, siblings, spouses, and children, comprising 314,819 individuals. Our analyses provide strong evidence that longevity is transmitted as a quantitative genetic trait among survivors up to the top 10% of their birth cohort. We subsequently show a survival advantage, mounting to 31%, for individuals with top 10% surviving first and second-degree relatives in both databases and across generations, even in the presence of non-longevous parents. To guide future genetic studies, we suggest to base case selection on top 10% survivors of their birth cohort with equally long-lived family members.
Globally, the lifespan of populations increases but the healthspan is lagging behind. Previous research showed that survival into extreme ages (longevity) clusters in families as illustrated by the ...increasing lifespan of study participants with each additional long-lived family member. Here we investigate whether the healthspan in such families follows a similar quantitative pattern using three-generational data from two databases, LLS (Netherlands), and SEDD (Sweden). We study healthspan in 2143 families containing index persons with 26 follow-up years and two ancestral generations, comprising 17,539 persons. Our results provide strong evidence that an increasing number of long-lived ancestors associates with up to a decade of healthspan extension. Further evidence indicates that members of long-lived families have a delayed onset of medication use, multimorbidity and, in mid-life, healthier metabolomic profiles than their partners. We conclude that both lifespan and healthspan are quantitatively linked to ancestral longevity, making family data invaluable to identify protective mechanisms of multimorbidity.
Facial pigmented spots are a common skin aging feature, but genetic predisposition has yet to be thoroughly investigated. We conducted a genome-wide association study for pigmented spots in 2,844 ...Dutch Europeans from the Rotterdam Study (mean age: 66.9±8.0 years; 47% male). Using semi-automated image analysis of high-resolution digital facial photographs, facial pigmented spots were quantified as the percentage of affected skin area (mean women: 2.0% ±0.9, men: 0.9% ±0.6). We identified genome-wide significant association with pigmented spots at three genetic loci: IRF4 (rs12203592, P=1.8 × 10−27), MC1R (compound heterozygosity score, P=2.3 × 10−24), and RALY/ASIP (rs6059655, P=1.9 × 10−9). In addition, after adjustment for the other three top-associated loci the BNC2 locus demonstrated significant association (rs62543565, P=2.3 × 10−8). The association signals observed at all four loci were successfully replicated (P<0.05) in an independent Dutch cohort (Leiden Longevity Study n=599). Although the four genes have previously been associated with skin color variation and skin cancer risk, all association signals remained highly significant (P<2 × 10−8) when conditioning the association analyses on skin color. We conclude that genetic variations in IRF4, MC1R, RALY/ASIP, and BNC2 contribute to the acquired amount of facial pigmented spots during aging, through pathways independent of the basal melanin production.
Summary
Middle‐aged offspring of nonagenarians, as compared to their spouses (controls), show a favorable lipid metabolism marked by larger LDL particle size in men and lower total triglyceride ...levels in women. To investigate which specific lipids associate with familial longevity, we explore the plasma lipidome by measuring 128 lipid species using liquid chromatography coupled to mass spectrometry in 1526 offspring of nonagenarians (59 years ± 6.6) and 675 (59 years ± 7.4) controls from the Leiden Longevity Study. In men, no significant differences were observed between offspring and controls. In women, however, 19 lipid species associated with familial longevity. Female offspring showed higher levels of ether phosphocholine (PC) and sphingomyelin (SM) species (3.5–8.7%) and lower levels of phosphoethanolamine PE (38:6) and long‐chain triglycerides (TG) (9.4–12.4%). The association with familial longevity of two ether PC and four SM species was independent of total triglyceride levels. In addition, the longevity‐associated lipid profile was characterized by a higher ratio of monounsaturated (MUFA) over polyunsaturated (PUFA) lipid species, suggesting that female offspring have a plasma lipidome less prone to oxidative stress. Ether PC and SM species were identified as novel longevity markers in females, independent of total triglycerides levels. Several longevity‐associated lipids correlated with a lower risk of hypertension and diabetes in the Leiden Longevity Study cohort. This sex‐specific lipid signature marks familial longevity and may suggest a plasma lipidome with a better antioxidant capacity, lower lipid peroxidation and inflammatory precursors, and an efficient beta‐oxidation function.
There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual's true global physiological ...state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one's chronological age-independent aging divergence ∆.
We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold.
The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging's association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random.
Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.
Immunoglobulin G (IgG), a glycoprotein secreted by plasma B-cells, plays a major role in the human adaptive immune response and are associated with a wide range of diseases. Glycosylation of the Fc ...binding region of IgGs, responsible for the antibody's effector function, is essential for prompting a proper immune response. This study focuses on the general genetic impact on IgG glycosylation as well as corresponding subclass specificities. To identify genetic loci involved in IgG glycosylation, we performed a genome-wide association study (GWAS) on liquid chromatography electrospray mass spectrometry (LC-ESI-MS)-measured IgG glycopeptides of 1,823 individuals in the Cooperative Health Research in the Augsburg Region (KORA F4) study cohort. In addition, we performed GWAS on subclass-specific ratios of IgG glycans to gain power in identifying genetic factors underlying single enzymatic steps in the glycosylation pathways. We replicated our findings in 1,836 individuals from the Leiden Longevity Study (LLS). We were able to show subclass-specific genetic influences on single IgG glycan structures. The replicated results indicate that, in addition to genes encoding for glycosyltransferases (i.e.,
, and
), other genetic loci have strong influences on the IgG glycosylation patterns. A novel locus on chromosome 1, harboring
, which encodes for a transcription factor of the runt domain-containing family, is associated with decreased galactosylation. Interestingly, members of the
family are cross-regulated, and
is involved in both IgA class switching and B-cell maturation as well as T-cell differentiation and apoptosis. Besides the involvement of glycosyltransferases in IgG glycosylation, we suggest that, due to the impact of variants within
, potentially mechanisms involved in B-cell activation and T-cell differentiation during the immune response as well as cell migration and invasion involve IgG glycosylation.
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