Abstract
With the aging of the rural population in Ecuador, this population is exposed to malnutrition, sarcopenia, and functional decline, associated with difficulty accessing health services and poor socioeconomic conditions. Electrical bioimpedance (BIA) allows estimating body composition; the Mini Nutritional Assessment (MNA) establishes the nutritional risk of older adults. To examine the association between body composition, nutritional status, functionality, and chronic diseases in older adults from three rural parishes of Cuenca, Ecuador. Cross-sectional study in 302 older adults, with a mean age of 74.3 ± 7.6 years. Two body composition profiles were established using the k-means technique in cluster analysis. Comparison tests, Spearman correlations, and ROC were used for associations and predictive capacity. 56.5% presented risk of malnutrition according to the MNA. Body composition showed no significant correlations with the MNA or discriminative capacity for predicting nutritional risk (AUC: 0.479–0.540). Nutritional risk was significantly associated with worse balance and gait, as well as with greater risk of falls and functional dependence. BMI and fat mass were associated with hypertension and diabetes, while lower lean mass and skeletal muscle were associated with greater functional dependence. In this Andean rural population, BIA allowed identification of overweight, obesity, and associations with cardiometabolic diseases, although it should not be considered an adequate isolated indicator of malnutrition risk. Comprehensive geriatric assessment, and especially functional status and fall risk, should accompany nutritional screening in primary rural care to guide preventive decisions and improve appropriate clinical follow-up.
References
Baumgart, M., Snyder, H. M., Carrillo, M. C., Fazio, S., Kim, H., & Johns, H. (2015). Summary of the evidence on modifiable risk factors for cognitive decline and dementia: A population-based perspective. Alzheimer’s & Dementia, 11(6), 718–726. https://doi.org/10.1016/j.jalz.2015.05.016
Beeri, M. S., Rapp, M., Silverman, J. M., Schmeidler, J., Grossman, H. T., Fallon, J. T. (2006). Coronary artery disease and Alzheimer pathology. Neurology, 66(9), 1399–1404. https://doi.org/10.1212/01.wnl.0000210533.20484.d0
Buchman, A. S., Boyle, P. A., Wilson, R. S., Tang, Y., & Bennett, D. A. (2007). Frailty and incident Alzheimer disease. Psychosomatic Medicine, 69(5), 483–489. https://doi.org/10.1097/PSY.0b013e318068de1d
Buracchio, T. J., Dodge, H. H., Howieson, D., Wasserman, D., & Kaye, J. (2010). The trajectory of gait speed preceding mild cognitive impairment. Archives of Neurology, 67(8), 980–986. https://doi.org/10.1001/archneurol.2010.159
Cederholm, T., Jensen, G. L., Correia, M. I. T. D., Gonzalez, M. C., Fukushima, R., Higashiguchi, T. (2019). GLIM criteria for the diagnosis of malnutrition: A consensus report from the global clinical nutrition community. Clinical Nutrition, 38(1), 1–9. https://doi.org/10.1016/j.clnu.2018.08.002
Cruz-Jentoft, A. J., Bahat, G., Bauer, J., Boirie, Y., Bruyère, O., Cederholm, T. (2019). Sarcopenia: Revised European consensus on definition and diagnosis. Age and Ageing, 48(1), 16–31. https://doi.org/10.1093/ageing/afy169
Debette, S., Seshadri, S., Beiser, A., Au, R., Himali, J. J., Palumbo, C. (2011). Midlife vascular risk factor exposure accelerates structural brain aging. Neurology, 77(5), 461–468. https://doi.org/10.1212/WNL.0b013e318227b227
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021
Fitzpatrick, A. L., Kuller, L. H., Lopez, O. L., Diehr, P., O’Meara, E. S., Longstreth, W. T., Jr. (2009). Midlife and late-life obesity and the risk of dementia. Archives of Neurology, 66(3), 336–342. https://doi.org/10.1001/archneurol.2008.582
Heymsfield, S. B., Peterson, C. M., Thomas, D. M., Heo, M., & Schuna, J. M., Jr. (2016). Why are there race/ethnic differences in adult body mass index–adiposity relationships? A quantitative critical review. Obesity Reviews, 17(3), 262–275. https://doi.org/10.1111/obr.12358
Jack, C. R., Jr., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W. (2013). Tracking pathophysiological processes in Alzheimer’s disease: An updated hypothetical model of dynamic biomarkers. The Lancet Neurology, 12(2), 207–216. https://doi.org/10.1016/S1474-4422(12)70291-0
Kyle, U. G., Bosaeus, I., De Lorenzo, A. D., Deurenberg, P., Elia, M., Gómez, J. M. (2004). Bioelectrical impedance analysis—Part I: Review of principles and methods. Clinical Nutrition, 23(5), 1226–1243. https://doi.org/10.1016/j.clnu.2004.06.004
Kyle, U. G., Bosaeus, I., De Lorenzo, A. D., Deurenberg, P., Elia, M., Gómez, J. M. (2004). Bioelectrical impedance analysis—Part II: Utilization in clinical practice. Clinical Nutrition, 23(6), 1430–1453. https://doi.org/10.1016/j.clnu.2004.09.012
Livingston, G., Huntley, J., Sommerlad, A., Ames, D., Ballard, C., Banerjee, S. (2020). Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet, 396(10248), 413–446. https://doi.org/10.1016/S0140-6736(20)30367-6
Pedditizi, E., Peters, R., & Beckett, N. (2016). The risk of overweight/obesity in mid-life and late life for the development of dementia: A systematic review and meta-analysis of longitudinal studies. Age and Ageing, 45(1), 14–21. https://doi.org/10.1093/ageing/afv151
Petersen, R. C., Roberts, R. O., Knopman, D. S., Boeve, B. F., Geda, Y. E., Ivnik, R. J. (2009). Mild cognitive impairment: Clinical characterization and outcome. Archives of Neurology, 66(12), 1447–1455. https://doi.org/10.1001/archneurol.2009.266
Ponti, F., Santoro, A., Mercatelli, D., Gasperini, C., Conte, M., Martucci, M. (2020). Aging and imaging assessment of body composition: From fat to facts. Frontiers in Endocrinology, 10. https://doi.org/10.3389/fendo.2019.00861
Prado, C. M., Purcell, S. A., & Laviano, A. (2020). Nutrition interventions to treat low muscle mass in cancer. Journal of Cachexia, Sarcopenia and Muscle, 11(2), 366–380. https://doi.org/10.1002/jcsm.12525
Prince, M., Bryce, R., Albanese, E., Wimo, A., Ribeiro, W., & Ferri, C. P. (2013). The global prevalence of dementia: A systematic review and metaanalysis. Alzheimer’s & Dementia, 9(1), 63–75. https://doi.org/10.1016/j.jalz.2012.11.007
Samuelsson, J., Marseglia, A., Wallengren, O., Lindberg, O., Dartora, C., Cedres, N. (2025). Association of body composition with neuroimaging biomarkers and cognitive function: A population-based study of 70-year-olds. eBioMedicine, 112. https://doi.org/10.1016/j.ebiom.2024.105555
Silveira, E. A., Barbosa, L. S., Rodrigues, A. P. S., Noll, M., & De Oliveira, C. (2020). Body fat percentage assessment by skinfold equation, bioimpedance and densitometry in older adults. Archives of Public Health, 78. https://doi.org/10.1186/s13690-020-00449-4
Srikanthan, P., & Karlamangla, A. S. (2014). Muscle mass index as a predictor of longevity in older adults. The American Journal of Medicine, 127(6), 547–553. https://doi.org/10.1016/j.amjmed.2014.02.007
Sternäng, O., Reynolds, C. A., Finkel, D., Ernsth-Bravell, M., Pedersen, N. L., & Dahl Aslan, A. K. (2015). Grip strength decline in older adults: Longitudinal findings from the Swedish Adoption/Twin Study of Aging. Age and Ageing, 44(2), 269–274. https://doi.org/10.1093/ageing/afu170
Wardlaw, J. M., Smith, E. E., Biessels, G. J., Cordonnier, C., Fazekas, F., Frayne, R. (2013). Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. The Lancet Neurology, 12(8), 822–838. https://doi.org/10.1016/S1474-4422(13)70124-8
Wells, J. C. K., & Fewtrell, M. S. (2006). Measuring body composition. Archives of Disease in Childhood, 91(7), 612–617. https://doi.org/10.1136/adc.2005.085522
Whitmer, R. A., Gunderson, E. P., Barrett-Connor, E., Quesenberry, C. P., Jr., & Yaffe, K. (2005). Obesity in middle age and future risk of dementia. BMJ, 330(7504). https://doi.org/10.1136/bmj.38446.466238.E0
World Health Organization. (2019). Risk reduction of cognitive decline and dementia: WHO guidelines.
Yoon, D. H., Lee, J. Y., & Song, W. (2018). Effects of resistance exercise training on cognitive function. Journal of Exercise Nutrition & Biochemistry, 22(3), 1–6. https://doi.org/10.20463/jenb.2018.0017

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