Luigi Ferrucci

National Institute on Aging, Baltimore, Maryland, United States

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Publications (936)6163.47 Total impact

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    ISMRM 2015; 06/2015
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    ABSTRACT: Some experts suggest that serum thyrotropin levels in the upper part of the current reference range should be considered abnormal, an approach that would reclassify many individuals as having mild hypothyroidism. Health hazards associated with such thyrotropin levels are poorly documented, but conflicting evidence suggests that thyrotropin levels in the upper part of the reference range may be associated with an increased risk of coronary heart disease (CHD). To assess the association between differences in thyroid function within the reference range and CHD risk. Individual participant data analysis of 14 cohorts with baseline examinations between July 1972 and April 2002 and with median follow-up ranging from 3.3 to 20.0 years. Participants included 55 412 individuals with serum thyrotropin levels of 0.45 to 4.49 mIU/L and no previously known thyroid or cardiovascular disease at baseline. Thyroid function as expressed by serum thyrotropin levels at baseline. Hazard ratios (HRs) of CHD mortality and CHD events according to thyrotropin levels after adjustment for age, sex, and smoking status. Among 55 412 individuals, 1813 people (3.3%) died of CHD during 643 183 person-years of follow-up. In 10 cohorts with information on both nonfatal and fatal CHD events, 4666 of 48 875 individuals (9.5%) experienced a first-time CHD event during 533 408 person-years of follow-up. For each 1-mIU/L higher thyrotropin level, the HR was 0.97 (95% CI, 0.90-1.04) for CHD mortality and 1.00 (95% CI, 0.97-1.03) for a first-time CHD event. Similarly, in analyses by categories of thyrotropin, the HRs of CHD mortality (0.94 [95% CI, 0.74-1.20]) and CHD events (0.97 [95% CI, 0.83-1.13]) were similar among participants with the highest (3.50-4.49 mIU/L) compared with the lowest (0.45-1.49 mIU/L) thyrotropin levels. Subgroup analyses by sex and age group yielded similar results. Thyrotropin levels within the reference range are not associated with risk of CHD events or CHD mortality. This finding suggests that differences in thyroid function within the population reference range do not influence the risk of CHD. Increased CHD risk does not appear to be a reason for lowering the upper thyrotropin reference limit.
    JAMA Internal Medicine 04/2015; DOI:10.1001/jamainternmed.2015.0930 · 13.25 Impact Factor
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    ABSTRACT: Abstract Background. Higher cardiorespiratory fitness (CRF) is cross-sectionally associated with more conserved brain volume in older age, but longitudinal studies are rare. This study examined whether higher midlife CRF was prospectively associated with slower atrophy, which in turn was associated with higher late-life CRF. Methods. Brain volume by magnetic resonance imaging was determined annually from 1994 to 2003 in 146 participants (M baseline age = 69.6 years). Peak oxygen uptake on a treadmill yielded estimated midlife CRF in 138 and late-life CRF in 73 participants. Results. Higher midlife CRF was associated with greater middle temporal gyrus, perirhinal cortex, and temporal and parietal white matter, but was not associated with atrophy progression. Slower atrophy in middle frontal and angular gyri was associated with higher late-life CRF, independent of CRF at baseline magnetic resonance imaging. Conclusions. Higher midlife CRF may play a role in preserving middle and medial temporal volumes in late adulthood. Slower atrophy in middle frontal and angular gyri may predict late-life CRF.
    The Journals of Gerontology Series A Biological Sciences and Medical Sciences 04/2015; DOI:10.1093/gerona/glv041 · 4.98 Impact Factor
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    PLoS ONE 04/2015; 10(4):e0122541. DOI:10.1371/journal.pone.0122541 · 3.53 Impact Factor
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    ABSTRACT: To determine the risk of stroke associated with subclinical hypothyroidism. Data Sources and Study Selection Published prospective cohort studies were identified through a systematic search through November 2013 without restrictions in several databases. Unpublished studies were identified through the Thyroid Studies Collaboration. We collected individual participant data (IPD) on thyroid function and stroke outcome. Euthyroidism was defined as thyrotropin (TSH) levels 0.45-4.49 mIU/L, subclinical hypothyroidism as TSH levels 4.5-19.9 mIU/L with normal thyroxin levels. Data Extraction and Synthesis We collected IPD on 47,573 adults (3451 subclinical hypothyroidism) from 17 cohorts, followed-up 1972-2014 (489,192 person-years). Age- and sex-adjusted pooled hazard ratio (HR) for participants with subclinical hypothyroidism compared to euthyroidism was 1.05 (95% CI, 0.91-1.21) for stroke events (combined fatal and non-fatal stroke) and 1.07 (95% CI, 0.80-1.42) for fatal stroke. Stratified by age, the HR for stroke events was 3.32 (95% CI, 1.25-8.80) for individuals aged 18-49 years. There was an increased risk of fatal stroke in the age groups 18-49 and 50-64 years with a HR of 4.22 (95% CI, 1.08-16.55) and 2.86 (95% CI, 1.31-6.26), respectively (p trend 0.04). We found no increased risk for those 65-79 years (HR 1.00, 95% CI, 0.86-1.18) or ≥80 years (HR 1.31, 95% CI, 0.79-2.18). There was a pattern of increased risk of fatal stroke with higher TSH concentrations. Although no overall effect of subclinical hypothyroidism on stroke could be demonstrated, an increased risk in subjects younger than 65 years and those with higher TSH concentrations was observed.
    The Journal of Clinical Endocrinology and Metabolism 04/2015; DOI:10.1210/jc.2015-1438 · 6.31 Impact Factor
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    ABSTRACT: Apolipoprotein E (APOE) genotype influences onset age of Alzheimer’s disease but effects on disease progression are less clear. We investigated amyloid-β (Aβ) levels and change in relationship to APOE genotype, using two different measures of Aβ in two different longitudinal cohorts. Aβ accumulation was measured using PET imaging and 11C-Pittsburgh compound-B (PiB) in 113 Baltimore Longitudinal Study of Aging (BLSA) participants (mean age 77.3 years; 107 normal, 6 cognitively impaired) and CSF Aβ1-42 assays in 207 BIOCARD study participants (mean age 62 years; 195 normal,12 cognitively impaired). Participants in both cohorts had up to 7 serial assessments (mean 2.3-2.4). PET-PiB retention increased and CSF Aβ1-42 declined longitudinally. APOE ε4 was significantly associated with higher PET-PiB retention and lower CSF Aβ1-42, independent of age and sex, but APOE genotype did not significantly affect Aβ change over time. APOE ε4 carriers may be further along in the disease process, consistent with earlier brain Aβ deposition and providing a biological basis for APOE genotype effects on onset age of Alzheimer’s disease.
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    ABSTRACT: Frailty, an age-related state of increased vulnerability, is associated with a higher risk of multiple adverse events. Studies have suggested that the quality of dietary intake may affect the development of frailty. We hypothesized that frailty in older subjects would be associated with dietary total polyphenols (DTP) intake and its biomarker, urinary total polyphenols (UTP). The Invecchiare in Chianti (InCHIANTI) Study is a prospective cohort study set in the Chianti area (Italy). We used data at baseline from 811 participants aged 65 years and older. UTP was determined using the Folin-Ciocalteu assay after solid-phase extraction. DTP was estimated using a validated Food Frequency Questionnaire and our own polyphenol database. The frailty, prefrailty, and nonfrailty states were defined according to the Fried and colleagues' criteria. Multinomial logistic regressions adjusted for potential confounders were used to assess the relationship between polyphenols and frailty. Both DTP and UTP concentrations progressively decrease from nonfrail to frail participants. Participants in the highest UTP tertile compared to those in the lowest tertile were significantly less likely to be both frail (odds ratio [OR] = 0.36 [0.14-0.88], p = .025) and prefrail (OR = 0.64 [0.42-0.98], p = .038). Exhaustion and slowness were the only individual frailty criteria significantly associated with UTP tertiles. No significant association was observed between frailty and DTP, after adjustment for covariates. High concentrations of UTP were associated with lower prevalence of frailty and prefrailty in an older community-dwelling population. A polyphenol-rich diet may protect against frailty in older persons. Our findings should be confirmed in longitudinal studies. © The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail:
    The Journals of Gerontology Series A Biological Sciences and Medical Sciences 04/2015; DOI:10.1093/gerona/glv026 · 4.98 Impact Factor
  • The Journal of Infectious Diseases 03/2015; DOI:10.1093/infdis/jiv202 · 5.78 Impact Factor
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    ABSTRACT: Many studies of aging examine biomarkers one at a time, but complex systems theory and network theory suggest that interpretations of individual markers may be context-dependent. Here, we attempted to detect underlying processes governing the levels of many biomarkers simultaneously by applying principal components analysis to 43 common clinical biomarkers measured longitudinally in 3694 humans from three longitudinal cohort studies on two continents (Women's Health and Aging I & II, InCHIANTI, and the Baltimore Longitudinal Study on Aging). The first axis was associated with anemia, inflammation, and low levels of calcium and albumin. The axis structure was precisely reproduced in all three populations and in all demographic sub-populations (by sex, race, etc.); we call the process represented by the axis "integrated albunemia." Integrated albunemia increases and accelerates with age in all populations, and predicts mortality and frailty - but not chronic disease - even after controlling for age. This suggests a role in the aging process, though causality is not yet clear. Integrated albunemia behaves more stably across populations than its component biomarkers, and thus appears to represent a higher-order physiological process emerging from the structure of underlying regulatory networks. If this is correct, detection of this process has substantial implications for physiological organization more generally.
    PLoS ONE 03/2015; 10(3):e0116489. DOI:10.1371/journal.pone.0116489 · 3.53 Impact Factor
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    ABSTRACT: Mobility is an essential aspect of everyday life and enables autonomy and participation. Although many risk factors for mobility loss have been previously described, their relative importance and independent contributions to the long-term risk of losing mobility have not been well defined. This study is based on 1,013 men and women aged ≥65 years enrolled in 1998-2000 and followed for 9 years through 2007-2008 in the population-based InCHIANTI (Invecchiare in Chianti, aging in the Chianti area) study. We considered 44 different measures assessed at baseline to explore six subsystems: (i) central nervous system, (ii) peripheral nervous system, (iii) muscles, (iv) bone and joints, (v) energy production and delivery, and (vi) perceptual system. The outcome was incident mobility loss defined as self-report of inability to walk 400 m or climb and descend 10 steps without help from another person. Random survival forest analysis was used to rank the candidate predictors by their importance. The most important physiological markers predicting mobility loss that emerged from the random survival forest modeling were older age among women (81-95 vs 65-68 years, hazard ratio [HR] 9.60 [95% CI 3.35, 27.50]), weaker ankle dorsiflexion strength (lowest vs highest quintile, HR 5.25 [95% CI 2.35, 11.72]), low hip flexion range of motion (lowest vs highest quintile, HR 2.30 [95% CI 1.20, 4.41]), presence of primitive reflexes (yes vs no, HR 1.47 [95% CI 1.03, 2.09]), and tremor (yes vs no, HR 1.91 [95% CI 1.18, 3.07]). Prevention of mobility loss with aging should focus on prevention and treatment of neuromuscular impairments. © The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail:
    The Journals of Gerontology Series A Biological Sciences and Medical Sciences 03/2015; 70(5). DOI:10.1093/gerona/glv004 · 4.98 Impact Factor
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    ABSTRACT: The hormone klotho is encoded by aging-suppressor gene klotho and has multiple roles, including regulating mineral (calcium and phosphate) homeostasis. Vitamin D also regulates mineral homeostasis and upregulates klotho expression. Klotho positively relates to longevity, upper-extremity strength, and reduced disability in older adults; however, it is unknown whether circulating klotho relates to lower-extremity physical performance or whether the relation of vitamin D with physical performance is mediated by klotho. Klotho and 25-hydroxyvitamin D [25(OH)D] were measured in 860 participants aged ≥ 55 years in Invecchiare in Chianti, "Aging in Chianti" (InCHIANTI), a prospective cohort study comprising Italian adults. Lower-extremity physical performance was measured using the Short Physical Performance Battery, a summary score of balance, chair stand ability, and walking speed. Weighted estimating equations related plasma klotho and serum 25(OH)D concentrations measured at one visit to Short Physical Performance Battery measured longitudinally at multiple visits. Each additional natural log of klotho (pg/mL) was associated with 0.47 higher average Short Physical Performance Battery scores (95% confidence interval: 0.08 to 0.86, p value = .02) after adjustment for covariates, including 25(OH)D. Each natural log of 25(OH)D (ng/mL) was associated with 0.61 higher average Short Physical Performance Battery scores (95% confidence interval: 0.35 to 0.88, p value < .001) after adjustment for covariates, a result that changed little after adjustment for klotho. Plasma klotho and 25(OH)D both positively related to lower-extremity physical performance. However, the findings did not support the hypothesis that klotho mediates the relation of 25(OH)D with physical performance. Published by Oxford University Press on behalf of the Gerontological Society of America 2015.
    The Journals of Gerontology Series A Biological Sciences and Medical Sciences 03/2015; DOI:10.1093/gerona/glv017 · 4.98 Impact Factor
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    ABSTRACT: Genome-wide association studies (GWAS) have uncovered numerous genetic variants (SNPs) that are associated with blood pressure (BP). Genetic variants may lead to BP changes by acting on intermediate molecular phenotypes such as coded protein sequence or gene expression, which in turn affect BP variability. Therefore, characterizing genes whose expression is associated with BP may reveal cellular processes involved in BP regulation and uncover how transcripts mediate genetic and environmental effects on BP variability. A meta-analysis of results from six studies of global gene expression profiles of BP and hypertension in whole blood was performed in 7017 individuals who were not receiving antihypertensive drug treatment. We identified 34 genes that were differentially expressed in relation to BP (Bonferroni-corrected p<0.05). Among these genes, FOS and PTGS2 have been previously reported to be involved in BP-related processes; the others are novel. The top BP signature genes in aggregate explain 5%-9% of inter-individual variance in BP. Of note, rs3184504 in SH2B3, which was also reported in GWAS to be associated with BP, was found to be a trans regulator of the expression of 6 of the transcripts we found to be associated with BP (FOS, MYADM, PP1R15A, TAGAP, S100A10, and FGBP2). Gene set enrichment analysis suggested that the BP-related global gene expression changes include genes involved in inflammatory response and apoptosis pathways. Our study provides new insights into molecular mechanisms underlying BP regulation, and suggests novel transcriptomic markers for the treatment and prevention of hypertension.
    PLoS Genetics 03/2015; 11(3):e1005035. DOI:10.1371/journal.pgen.1005035 · 8.17 Impact Factor
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    ABSTRACT: Late-life depression and physical frailty are supposed to be reciprocally associated, however, longitudinal studies are lacking. This study examines whether physical frailty predicts a higher incidence of depression, as well as a less favorable course of depression. A population-based cohort study of 888 people aged 65 years and over with follow-up measures at 3, 6, and 9 years. Cox proportional hazards models adjusted for age, sex, education, smoking, alcohol usage, and global cognitive functioning were applied to calculate the incidence of depressed mood in those nondepressed at baseline (n = 699) and remission in those with depressed mood at baseline (n = 189). Depressed mood onset or remission was defined as crossing the cut-off score of 20 points on the Center for Epidemiological Studies-Depression Scale combined with a relevant change in this score. Physical frailty was based on the presence of ≥3 out of 5 components (ie, weight loss, weakness, slowness, exhaustion, and low physical activity level). A total of 214 out of 699 (30.6%) nondepressed persons developed depressed mood during follow-up. Physical frailty predicted the onset of depressed mood with a hazard rate of 1.26 (95% confidence interval 1.09-1.45, P = .002). Of the 189 persons with depressed mood at baseline, 96 (50.8%) experienced remission during follow-up. Remission was less likely in the presence of a higher level of physical frailty (hazard rate = 0.72, 95% confidence interval 0.58-0.91, P = .005). Because physical frailty predicts both the onset and course of late-life depressed mood, physical frailty should receive more attention in mental health care planning for older persons as well as its interference with treatment. Future studies into the pathophysiological mechanisms may guide the development of new treatment opportunities for these vulnerable patients. Copyright © 2015 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.
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    ABSTRACT: Obesity is a risk factor for decline in glomerular filtration rate (GFR). One proposed mechanism leading to glomerulopathy is an increase in leptin levels. However, the association between leptin and GFR has never been demonstrated. The aim of this study is to verify whether higher levels of leptin are associated with longitudinal changes of estimated GFR (eGFR). We selected 744 participants in the InCHIANTI study (416 women). The association between eGFR and leptin changes over a 6-years follow-up was assessed using random effect models including leptin as a time-varying covariate and adjusted for potential confounders. We also compared the proportion of patients with rapid decline of renal function across tertiles of change in serum leptin between baseline and 6-years follow-up. Mean baseline eGFR was 82.2 ml/min/1.73 m, 78.7 ml/min/1.73 m, and 75.4 ml/min/1.73 m in the first, second and third tertile of baseline serum leptin concentration, respectively. After adjustment for potential confounders, leptin concentration was inversely associated with changes of eGFR over time (β for log-leptin: -1.288, 95% CI: -2.079 - -0.497). Relative to baseline levels, the estimated change in eGFR for unit-increase in log-leptin was -1.9% (95% CI: -2.977 - -0.761). After stratification by sex, the results were confirmed in women only. In women we also found an association between increasing leptin concentration over time and rapid decline of renal function. In women, serum leptin may contribute to eGFR decline independently from obesity and diabetes mellitus, although a cause-effect relationship cannot be established due to the observational nature of our study. A better characterization of adipokine profile of obese individuals may shed light on the accelerated renal function decline reported in a proportion of high-risk obese individuals.
    PLoS ONE 02/2015; 10(2):e0117828. DOI:10.1371/journal.pone.0117828 · 3.53 Impact Factor
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    ABSTRACT: MicroRNAs are non-coding RNAs with roles in many cellular processes. Tissue-specific miRNA profiles associated with senescence have been described for several cell and tissue types. We aimed to characterise miRNAs involved in core, rather than tissue-specific, senescence pathways by assessment of common miRNA expression differences in two different cell types, with follow-up of predicted targets in human peripheral blood. MicroRNAs were profiled in early and late passage primary lung and skin fibroblasts to identify commonly-deregulated miRNAs. Expression changes of their bioinformatically-predicted mRNA targets were then assessed in both cell types and in human peripheral blood from elderly participants in the InCHIANTI study. 57/178 and 26/492 microRNAs were altered in late passage skin and lung cells respectively. Three miRNAs (miR-92a, miR-15b and miR-125a-3p) were altered in both tissues. 14 mRNA targets of the common miRNAs were expressed in lung and skin fibroblasts, of which two demonstrated up-regulation in late passage skin and lung cells (LYST; p = 0.02 [skin] and 0.02 [lung] INMT; p = 0.03 [skin] and 0.04 [lung]). ZMPSTE24 and LHFPL2 demonstrated altered expression in late passage skin cells only (p = 0.01 and 0.05 respectively). LHFPL2 was also positively correlated with age in peripheral blood (p value = 6.6 × 10(-5)). We find that the majority of senescence-associated miRNAs demonstrate tissue-specific effects. However, miRNAs showing common effects across tissue types may represent those associated with core, rather than tissue-specific senescence processes.
    Biogerontology 02/2015; DOI:10.1007/s10522-015-9560-5 · 3.01 Impact Factor
  • Joseph B. Margolick, Luigi Ferrucci
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    ABSTRACT: Claims of accelerated or premature aging are frequently made. However, the lack of standard criteria for measuring speed of aging makes such claims highly questionable. Because of fundamental gaps in our current understanding of the biological mechanisms of aging, the development of specific phenotypes that are due to aging is difficult and such phenotypes can only be derived by observational data. However, a clinical phenotype of aging exists that is experienced by all living individuals and is pervasive across multiple physiologic systems. Characterizing this phenotype can serve as a basis for measuring the speed of aging, and can facilitate a better understanding of the aging process and its interaction with chronic diseases. Copyright © 2015. Published by Elsevier Inc.
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    ABSTRACT: Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis
    Nature 02/2015; 518(7538). DOI:10.1038/nature14177 · 42.35 Impact Factor
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    ABSTRACT: Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 x 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.
    Nature 02/2015; 518(7538-7538):187-96. DOI:10.1038/nature14132 · 42.35 Impact Factor
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    ABSTRACT: Metabolic reprogramming of muscle stem cells modulates myogenic cell fate. In this issue of Cell Stem Cell, Ryall et al. (2015) show that SIRT1, a NAD(+)-dependent histone deacetylase, acts as an epigenetic regulator that connects changes in satellite cell metabolism with changes in the transcriptional machinery toward myogenic commitment. Copyright © 2015 Elsevier Inc. All rights reserved.
    Cell Stem Cell 02/2015; 16(2). DOI:10.1016/j.stem.2015.01.006 · 22.15 Impact Factor
  • Seung-Uk Ko, Eleanor M Simonsick, Luigi Ferrucci
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    ABSTRACT: With aging, customary gait patterns change and energetic efficiency declines, but the relationship between these alterations is not well understood. If gait characteristics that develop with aging explain part of the decline in energetic efficiency that occur in most aging individuals, then efforts to modify these characteristics could delay or prevent mobility limitation. This study characterizes gait patterns in older persons with and without knee pain and tests the hypothesis that changes in gait characteristics due to knee pain are associated with increased energetic cost of walking in older adults. Study participants were 364 men and 170 women aged 60 to 96 years enrolled in the Baltimore Longitudinal Study of Aging (BLSA), of whom 86 had prevalent knee pain. Gait patterns were assessed at participant self-selected usual pace in the gait laboratory, and the energetic cost of walking was assessed by indirect calorimetry during self-selected usual pace walking over 2.5 min in a tiled corridor using a portable equipment. Participants with knee pain were less energetically efficient than those without pain (oxygen consumption 0.97 vs. 0.88 ml/(10 m · 100 kg); p = 0.002) and had slower gait speed and smaller range of motion (ROM) at the hip and knee joints (p < 0.05, for all). Slower gait speed and lower knee ROM in participants with knee pain and longer double support time and higher ankle ROM in participants without knee pain were associated with lower energetic efficiency (p < 0.05, for all). Slower gait speed and lower knee ROM were correlates of knee pain and were found to mediate the association between age and oxygen consumption. Although knee pain is associated with a higher energetic cost of walking, gait characteristics associated with energetic efficiency differ by pain status which suggests that compensatory strategies both in the presence and absence of pain may impact gait efficiency.
    Journal of the American Aging Association 02/2015; 37(1):9754. DOI:10.1007/s11357-015-9754-4 · 3.45 Impact Factor

Publication Stats

36k Citations
6,163.47 Total Impact Points


  • 2001–2015
    • National Institute on Aging
      • • Clinical Research Branch (CRB)
      • • Laboratory of Epidemiology, Demography and Biometry (LEDB)
      Baltimore, Maryland, United States
  • 2014
    • Universiteit Twente
      • Group of Behavioural Sciences
      Enschede, Overijssel, Netherlands
    • California Pacific Medical Center Research Institute
      • Research Institute
      San Francisco, California, United States
  • 2003–2014
    • National Institutes of Health
      • • Clinical Research Branch (CRB)
      • • Laboratory of Immunology
      • • Laboratory of Epidemiology, Demography, and Biometry (LEDB)
      베서스다, Maryland, United States
    • Texas A&M University - Galveston
      Galveston, Texas, United States
    • University of Iowa
      Iowa City, Iowa, United States
    • AMC Health
      New York City, New York, United States
    • Catholic University of the Sacred Heart
      • School of Geriatrics
      Milano, Lombardy, Italy
    • INRIM Istituto Nazionale di Ricerca Metrologica
      Torino, Piedmont, Italy
  • 2013
    • Università degli Studi di Salerno
      Fisciano, Campania, Italy
    • Northern Inyo Hospital
      BIH, California, United States
    • Harvard Medical School
      Boston, Massachusetts, United States
    • Florida State University
      • Department of Geriatrics
      Tallahassee, Florida, United States
    • Universita degli studi di Ferrara
      • Section of Internal Medicine, Gerontology and Geriatrics
      Ferrare, Emilia-Romagna, Italy
  • 2010–2013
    • Queen's University
      • School of Rehabilitation Therapy
      Kingston, Ontario, Canada
    • Kent State University
      • Department of Psychology
      Kent, OH, United States
    • University Hospital of Parma
      Parma, Emilia-Romagna, Italy
    • Northwestern University
      • Feinberg School of Medicine
      Evanston, IL, United States
    • University of California, San Francisco
      • Division of Hospital Medicine
      San Francisco, CA, United States
    • University of Lausanne
      • Faculté de biologie et de médecine (FBM)
      Lausanne, VD, Switzerland
  • 2004–2013
    • Università degli studi di Parma
      • Department of Clinical and Experimental Medicine
      Parma, Emilia-Romagna, Italy
    • Università degli Studi di Sassari
      Sassari, Sardinia, Italy
    • Wake Forest School of Medicine
      • Sticht Center on Aging
      Winston-Salem, NC, United States
  • 2012
    • National Institute of Aerospace
      Hampton, Virginia, United States
    • University-Hospital of Padova
      Padua, Veneto, Italy
    • McGill University
      • Department of Epidemiology, Biostatistics and Occupational Health
      Montréal, Quebec, Canada
    • University of Missouri - St. Louis
      Saint Louis, Michigan, United States
  • 2009–2012
    • University of Pittsburgh
      • Division of Geriatric Medicine
      Pittsburgh, Pennsylvania, United States
    • National University Hospital of Iceland
      Reikiavik, Capital Region, Iceland
    • National Eye Institute
      Maryland, United States
    • Case Western Reserve University
      • School of Medicine
      Cleveland, Ohio, United States
    • University Hospital Regensburg
      Ratisbon, Bavaria, Germany
    • MedStar Health Research Institute
      Maryland, United States
  • 2006–2012
    • University of Pennsylvania
      • • Division of Geriatric Medicine
      • • Center for Clinical Epidemiology and Biostatistics
      Filadelfia, Pennsylvania, United States
    • Università degli Studi di Palermo
      Palermo, Sicily, Italy
    • Fondazione Don Carlo Gnocchi
      Milano, Lombardy, Italy
    • University of Florida
      • Department of Aging and Geriatric Research
      Gainesville, FL, United States
    • University of Maryland, College Park
      • Department of Kinesiology
      College Park, MD, United States
  • 2004–2012
    • Johns Hopkins University
      • • Department of Medicine
      • • Welch Center for Prevention, Epidemiology, and Clinical Research
      • • Division of Geriatric Medicine and Gerontology
      Baltimore, Maryland, United States
  • 2003–2012
    • University of Maryland, Baltimore
      • • Department of Epidemiology and Public Health
      • • Department of Medicine
      Baltimore, Maryland, United States
  • 2002–2012
    • Johns Hopkins Medicine
      • • Department of Otolaryngology - Head and Neck Surgery
      • • Department of Urology
      Baltimore, Maryland, United States
  • 2011
    • Yale University
      New Haven, Connecticut, United States
    • National Institute for Health and Welfare, Finland
      • Department of Health, Functional Capacity and Welfare
      Helsinki, Province of Southern Finland, Finland
    • Boston University
      • Department of Biostatistics
      Boston, Massachusetts, United States
    • University of Leicester
      • Department of Health Sciences
      Leicester, ENG, United Kingdom
    • Università degli Studi di Brescia
      • Department of Clinical and Experimental Sciences
      Brescia, Lombardy, Italy
  • 2009–2011
    • University of Exeter
      • • Peninsula College of Medicine and Dentistry
      • • Department of Biosciences
      Exeter, England, United Kingdom
  • 2005–2011
    • University of Naples Federico II
      • Department of Molecular Medicine and Medical Biotechnology
      Napoli, Campania, Italy
    • Johnson & Johnson
      New Brunswick, New Jersey, United States
    • Università Vita-Salute San Raffaele
      Milano, Lombardy, Italy
    • IMIM Hospital del Mar Medical Research Institute
      Barcino, Catalonia, Spain
  • 2009–2010
    • IRCCS Ospedale Casa Sollievo della Sofferenza
      • Department of Medical Sciences
      Giovanni Rotondo, Apulia, Italy
  • 2006–2010
    • Azienda Sanitaria di Firenze
      Florens, Tuscany, Italy
  • 2005–2009
    • Second University of Naples
      Caserta, Campania, Italy
    • Università degli Studi di Perugia
      • Department of Clinical and Experimental Medicine
      Perugia, Umbria, Italy
  • 2004–2009
    • Johns Hopkins Bloomberg School of Public Health
      • • Department of Biostatistics
      • • Department of Health Policy and Management
      Baltimore, MD, United States
  • 1996–2009
    • INRCA Istituto Nazionale di Ricovero e Cura per Anziani
      • Gerontological Research Department
      Ancona, The Marches, Italy
  • 2008
    • Grays Harbor Community Hospital
      Aberdeen, Washington, United States
    • Boston Medical Center
      Boston, Massachusetts, United States
    • Saint Louis University
      Saint Louis, Michigan, United States
    • Universidade Federal do Rio Grande do Sul
      Pôrto de São Francisco dos Casaes, Rio Grande do Sul, Brazil
  • 2006–2008
    • University of California, San Diego
      • Department of Family and Preventive Medicine
      San Diego, CA, United States
  • 2003–2008
    • Università degli Studi G. d'Annunzio Chieti e Pescara
      Chieta, Abruzzo, Italy
  • 2002–2008
    • University of Washington Seattle
      • Department of Rehabilitation Medicine
      Seattle, Washington, United States
  • 2007
    • The Peninsula College of Medicine and Dentistry
      Plymouth, England, United Kingdom
    • Unità Locale Socio Sanitaria Padova ULSS 16
      Padua, Veneto, Italy
    • VU University Medical Center
      • Department of Psychiatry
      Amsterdamo, North Holland, Netherlands
    • Duke University Medical Center
      Durham, North Carolina, United States
  • 2005–2007
    • Cornell University
      • Department of Nutritional Sciences
      Итак, New York, United States
  • 1986–1988
    • University of Florence
      • Dipartimento di Chirurgia e Medicina Traslazionale (DCMT)
      Florens, Tuscany, Italy