Megan M Marron1, Iva Miljkovic1, Robert M Boudreau1, Kaare Christensen2, Mary F Feitosa3, Joseph H Lee4, Paola Sebastiani5, Bharat Thyagarajan6, Mary K Wojczynski3, Joseph M Zmuda7, Anne B Newman8. 1. Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA. 2. The Danish Aging Research Center, Department of Public Health, University of Southern Denmark, Odense, Denmark. 3. Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA. 4. Taub Institute for Research on Alzheimer's Disease and the Aging Brain, and Sergievsky Center, Columbia University Medical Center, New York, NY, USA. 5. Department of Biostatistics, Boston University, Boston, MA, USA. 6. Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA. 7. Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA; Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA. 8. Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA; Departments of Medicine and Clinical and Translational Science, University of Pittsburgh, Pittsburgh, PA, USA. Electronic address: newmana@edc.pitt.edu.
Abstract
BACKGROUND: Long-lived individuals and their offspring have healthier metabolic characteristics than expected, such as more favorable levels of fasting glucose, insulin, and lipids than controls without longevity. Dysregulation in metabolic pathways has also shown to predict accelerated aging. Using information from the Long Life Family Study (LLFS), a multi-center study of two-generation families selected for exceptional longevity, we developed an indicator of healthy metabolism to determine whether metabolic health was more prevalent in a subset of LLFS families and whether it was heritable and associated with other metrics of healthy aging. METHODS: A Latent Profile Analysis was applied to age- and gender-adjusted z-scores of fasting levels of glucose, insulin, triglycerides, and high-density lipoprotein cholesterol, body mass index, waist circumference, interleukin-6, and C-reactive protein. Families were defined as meeting the healthy metabolic phenotype if ≥2 and ≥50% of their offspring were classified into a latent subgroup with a profile of healthier metabolic markers than expected given age and gender relative to all LLFS offspring. RESULTS: The log odds of being classified into the latent subgroup with a healthy profile of metabolic markers was heritable (h2 = 0.40, p < 0.001). Among 388 families, 39 (10%) met the healthy metabolic phenotype. Participants from these families had somewhat better cognition than those from remaining families. Proband-generation participants from families who met the healthy metabolic phenotype also had better pulmonary functioning and physical performance. CONCLUSIONS: The better cognition, pulmonary function, and physical performance among probands from families with the healthy metabolic phenotype may indicate that this subset of LLFS families have a more extreme longevity phenotype than other LLFS families since cognitive, physical, and pulmonary function are top mortality predictors for older adults. Future work is needed to determine if rare or protective alleles confer a healthy metabolic phenotype in this subset of LLFS families with exceptional metabolism.
BACKGROUND: Long-lived individuals and their offspring have healthier metabolic characteristics than expected, such as more favorable levels of fasting glucose, insulin, and lipids than controls without longevity. Dysregulation in metabolic pathways has also shown to predict accelerated aging. Using information from the Long Life Family Study (LLFS), a multi-center study of two-generation families selected for exceptional longevity, we developed an indicator of healthy metabolism to determine whether metabolic health was more prevalent in a subset of LLFS families and whether it was heritable and associated with other metrics of healthy aging. METHODS: A Latent Profile Analysis was applied to age- and gender-adjusted z-scores of fasting levels of glucose, insulin, triglycerides, and high-density lipoprotein cholesterol, body mass index, waist circumference, interleukin-6, and C-reactive protein. Families were defined as meeting the healthy metabolic phenotype if ≥2 and ≥50% of their offspring were classified into a latent subgroup with a profile of healthier metabolic markers than expected given age and gender relative to all LLFS offspring. RESULTS: The log odds of being classified into the latent subgroup with a healthy profile of metabolic markers was heritable (h2 = 0.40, p < 0.001). Among 388 families, 39 (10%) met the healthy metabolic phenotype. Participants from these families had somewhat better cognition than those from remaining families. Proband-generation participants from families who met the healthy metabolic phenotype also had better pulmonary functioning and physical performance. CONCLUSIONS: The better cognition, pulmonary function, and physical performance among probands from families with the healthy metabolic phenotype may indicate that this subset of LLFS families have a more extreme longevity phenotype than other LLFS families since cognitive, physical, and pulmonary function are top mortality predictors for older adults. Future work is needed to determine if rare or protective alleles confer a healthy metabolic phenotype in this subset of LLFS families with exceptional metabolism.
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