Metabolic profiles of biological aging in American Indians: The strong heart family study

Zhao, Jinying; Zhu, Yun; Uppal, Karan; Tran, ViLinh; Yu, Tianwei; Lin, Jue; Matsuguchi, Tet; Blackburn, Elizabeth H.; Jones, Dean P.; Lee, Elisa T.; Howard, Barbara V.

doi:10.18632/aging.100644

Cited by 23 publications

(20 citation statements)

References 57 publications

(60 reference statements)

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“…Pro-inflammatory properties of leptin have been shown [21], with binding to its receptor leading to activation of signalling pathways involved in chronic inflammation and age-related diseases such as JAK/STAT and NF-κB [22]. Corroborating a role of obesity in ageing, we found further evidence linking excess adiposity and shorter telomere length which echoed some previous findings [15, 23–26]. Despite the suggested association, a Mendelian randomisation analysis comprising Danish populations found no evidence of a causal association between BMI and telomere length [24].…”

Section: Discussionsupporting

confidence: 89%

Investigating the associations between adiposity, life course overweight trajectories, and telomere length

Wulaningsih

Watkins²,

Matsuguchi

et al. 2016

Aging

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Obesity may accelerate ageing through chronic inflammation. To further examine this association, we assessed current adiposity, adiposity at early adulthood and life course overweight trajectories in relation to leukocyte telomere length (LTL). We included a total of 7,008 nationally representative U.S. residents and collected information on objectively measured body mass index (BMI), waist circumference and percent body fat. BMI at age 25 and overweight trajectories were assessed using self-reported history. Leukocyte telomere length (LTL) relative to a standard DNA reference (T/S ratio) was quantified by polymerase chain reaction (PCR). Linear regression models were used to examine the difference in LTL across adiposity measures at examination, BMI at age 25, and overweight trajectories. A 0.2% decrease in telomere length (95% CI:−0.3 to −0.07%) was observed for every kg/m2 increase in BMI, whereas a unit increase in waist circumference (cm) and percent body fat contributed to a 0.09% and 0.01% decrease in LTL, respectively. Higher BMI and being obese at age 25 contributed to lower LTL at older ages. Associations between weight loss through life course and LTL were observed, which further marked the importance of life course adiposity dynamics as a determinant of ageing.

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Section: Discussionsupporting

confidence: 89%

Investigating the associations between adiposity, life course overweight trajectories, and telomere length

Wulaningsih

Watkins²,

Matsuguchi

et al. 2016

Aging

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“…Thus, although blood sugar and kidney function clearly contribute to cardiovascular risk, their dysregulation in the general population does not seem to be captured by LTL, with the exception of some of our models showing a weak association with cystatin C. Again, however, it is important to consider that we used only established clinical risk factors for cardiovascular disease to capture the association of LTL with different physiological systems. Work taking a metabolomics approach has identified more specific biological components associated with LTL [57]. …”

Section: Discussionmentioning

confidence: 99%

Leukocyte Telomere Length in Relation to 17 Biomarkers of Cardiovascular Disease Risk: A Cross-Sectional Study of US Adults

et al. 2016

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BackgroundLeukocyte telomere length (LTL) is a putative biological marker of immune system age, and there are demonstrated associations between LTL and cardiovascular disease. This may be due in part to the relationship of LTL with other biomarkers associated with cardiovascular disease risk. However, the strength of associations between LTL and adiposity, metabolic, proinflammatory, and cardiovascular biomarkers has not been systematically evaluated in a United States nationally representative population.Methods and FindingsWe examined associations between LTL and 17 cardiovascular biomarkers, including lipoproteins, blood sugar, circulatory pressure, proinflammatory markers, kidney function, and adiposity measures, in adults ages 20 to 84 from the cross-sectional US nationally representative 1999–2002 National Health and Nutrition Examination Survey (NHANES) (n = 7,252), statistically adjusting for immune cell type distributions. We also examine whether these associations differed systematically by age, race/ethnicity, gender, education, and income. We found that a one unit difference in the following biomarkers were associated with kilobase pair differences in LTL: BMI -0.00478 (95% CI -0.00749–-0.00206), waist circumference -0.00211 (95% CI -0.00325–-0.000969), percentage of body fat -0.00516 (95% CI -0.00761–-0.0027), high density lipoprotein (HDL) cholesterol 0.00179 (95% CI 0.000571–0.00301), triglycerides -0.000285 (95% CI -0.000555–-0.0000158), pulse rate -0.00194 (95% CI -0.00317–-0.000705), C-reactive protein -0.0363 (95% CI 0.0601–-0.0124), cystatin C -0.0391 (95% CI -0.0772–-0.00107). When using clinical cut-points we additionally found associations between LTL and insulin resistance -0.0412 (95% CI -0.0685–-0.0139), systolic blood pressure 0.0455 (95% CI 0.00137–0.0897), and diastolic blood pressure -0.0674 (95% CI -0.126–-0.00889). These associations were 10%–15% greater without controlling for leukocyte cell types. There were very few differences in the associations by age, race/ethnicity, gender, education, or income. Our findings are relevant to the relationships between these cardiovascular biomarkers in the general population but not to cardiovascular disease as a clinical outcome.ConclusionsLTL is most strongly associated with adiposity, but is also associated with biomarkers across several physiological systems. LTL may thus be a predictor of cardiovascular disease through its association with multiple risk factors that are physiologically correlated with risk for development of cardiovascular disease. Our results are consistent with LTL being a biomarker of cardiovascular aging through established physiological mechanisms.

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“…Specifically, 2 lysolipids, 1-stearoylglycerophosphpinositol and 1-palmitoylglycerophosphoinositol were found to be negatively associated to LTL along with two ␥glutamylamino acids (Supplementary information S1) [48]. Altered lipid metabolism and its association with LTL is also evident from American Indian population, with 19 metabolites enriched in lipids (glycerophosphoethanolamines, glycerophosphocholines, glycerolipids, bile acids, isoprenoids, fatty amides, or L-carnitine ester, Supplementary information S1), were significantly associated with LTL [49].…”

Section: Aging Metabolomics Studies In Humans and Non-human Primatesmentioning

confidence: 98%

“…We posit that sleep restriction/deprivation leads to a metabolic setting that could resemble aging and may serve as lead to understand the connection of sleep loss and incident neurodegenerative disorders. Comparison of some recent aging metabolomics studies [47][48][49][50][51][52] and sleep metabolomics studies [61] reveals a striking signature of metabolic similarity. We have analyzed recent aging metabolomics literatures ( Fig.…”

Section: Metabolomics Studies Of Experimental Sleep Deprivationmentioning

confidence: 99%

Metabolism of sleep and aging: Bridging the gap using metabolomics

Sengupta

Weljie

2019

NHA

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Sleep is a conserved behavior across the evolutionary timescale. Almost all known animal species demonstrate sleep or sleep like states. Despite extensive study, the mechanistic aspects of sleep need are not very well characterized. Sleep appears to be needed to generate resources that are utilized during the active stage/wakefulness as well as clearance of waste products that accumulate during wakefulness. From a metabolic perspective, this means sleep is crucial for anabolic activities. Decrease in anabolism and build-up of harmful catabolic waste products is also a hallmark of aging processes. Through this lens, sleep and aging processes are remarkably parallel-for example behavioral studies demonstrate an interaction between sleep and aging. Changes in sleep behavior affect neurocognitive phenotypes important in aging such as learning and memory, although the underlying connections are largely unknown. Here we draw inspiration from the similar metabolic effects of sleep and aging and posit that large scale metabolic phenotyping, commonly known as metabolomics, can shed light to interleaving effects of sleep, aging and progression of diseases related to aging. In this review, data from recent sleep and aging literature using metabolomics as principal molecular phenotyping methods is collated and compared. The present data suggests that metabolic effects of aging and sleep also demonstrate similarities, particularly in lipid metabolism and amino acid metabolism. Some of these changes also overlap with metabolomic data available from clinical studies of Alzheimer's disease. Together, metabolomic technologies show promise in elucidating interleaving effects of sleep, aging and progression of aging disorders at a molecular level. process of aging is in part the result of progressive decline of physiological function at the molecular level. Key physiological processes impacted by aging include genomic instability, telomere attrition, epigenetic alteration, loss of proteastasis, nutrient sensing dysregulation, mitochondrial dysfunction, cellular senescence, stem cell exhaustion and altered intercellular communication [3].Along with molecular level changes, behavioral changes are also apparent with healthy aging. Memory impairment [4] and altered sleep wake activities are perhaps the most common signatures of aged individuals [4]. Both human population and rodent model studies have shown that aging individuals are unable to sustain sleep/wake state and generally

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Metabolic profiles of biological aging in American Indians: The strong heart family study

Cited by 23 publications

References 57 publications

Investigating the associations between adiposity, life course overweight trajectories, and telomere length

Investigating the associations between adiposity, life course overweight trajectories, and telomere length

Leukocyte Telomere Length in Relation to 17 Biomarkers of Cardiovascular Disease Risk: A Cross-Sectional Study of US Adults

Metabolism of sleep and aging: Bridging the gap using metabolomics

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