The relationship between sarcopenia and related bioindicators and changes after intensive lifestyle intervention in elderly East-China populations

Abstract

Background: As populations live longer, there is a progressive increase in chronic degenerative diseases, particularly those related to the musculoskeletal system. Sarcopenia is characterized by loss of skeletal muscle mass, muscle strength, and loss of physical function. It is a common disease in older adults associated with various adverse health outcomes. There is a lack of bioindicators to screen for sarcopenia. Albumin and lymphocyte counts are commonly used to assess the degree of malnutrition, and blood routine, lipids, and thyroid function are relatively easy to obtain as part of a routine physical examination. Therefore, finding blood markers that can screen for sarcopenia is essential. Our primary aim was to explore whether the bioindicators of body composition, lymphocytes, albumin, lipids, and thyroid hormones are associated with sarcopenia, and a secondary aim was to investigate changes in these indicators after an intensive lifestyle intervention preliminarily.

Methods: 60 subjects were selected from Runda and Bailian community health centers in Suzhou, China. They underwent body composition analysis and tested lymphocyte, albumin, lipid, and thyroid hormone levels. The 30 sarcopenia subjects underwent a 3-month intensive lifestyle intervention program. At the end of the intervention, we rechecked the bioindicators. Statistical analyses were performed in IBM SPSS v26.0.

Results: The blood indices of sarcopenia subjects were generally lower in albumin, non-high-density lipoprotein cholesterol (non-HDL-C), and free triiodothyronine (FT3). Body mass index (BMI)(r = 0.6266, p < 0.0001), fat-free mass (r = 0.8110, p < 0.0001), basal metabolism (r = 0.7782, p < 0.0001), and fat mass (r = 0.3916, p = 0.0020) were positively correlated with appendicular skeletal muscle index (ASMI). Higher BMI and FT3 were associated with lower odds of sarcopenia, while higher fat mass was associated with higher odds of sarcopenia. After a 3-month intensive intervention, sarcopenia subjects had a significant increase in BMI, ASMI, lymphocyte, and albumin levels, and an increase in FT3, but with a non-significant difference (p = 0.342).

Conclusions: Low BMI, FT3, and high fat mass were associated with sarcopenia. Intensive lifestyle intervention can significantly improve ASMI, BMI, lymphocytes, albumin, and FT3 in sarcopenia subjects, which is favorable for delaying the progression of sarcopenia.

Trial registration: This study was retrospectively registered on ClinicalTrials.gov, registration number NCT06128577, date of registration: 07/11/2023.

Keywords: Bioindicators; Exercise intervention; Intensive lifestyle intervention; Nutritional intervention; Sarcopenia; Whey protein.

Fig. 1

Study flowchart

Fig. 2

The frequency distributions of biological indicators of body composition and sarcopenia rates distributions among subjects. a BMI, body mass index; b fat-free mass; c fat mass; d basal metabolism. Each of the body composition indicators' ranges was further subdivided into four equal categories to form a frequency distribution for preliminary analysis. The graphs were created by GraphPad Prism

Fig. 3

The frequency distributions of blood bioindicators and sarcopenia rates distributions among subjects. a lymphocytes; b albumin; c TG, triglycerides; d TC, total cholesterol; e HDL-C, high-density lipoprotein cholesterol; f non-HDL-C, non-high-density lipoprotein cholesterol (this study refers to the sum of LDL-C and VLDL-C); g TSH, thyroid stimulating hormone; h FT4, free thyroxine; i FT3, free triiodothyronine. Each of the blood bioindicators' ranges was further subdivided into four equal categories to form a frequency distribution for preliminary analysis. The graphs were created by GraphPad Prism

Fig. 4

Correlation analysis of biological indicators of body composition levels vs. ASMI across all subjects. a BMI, body mass index; b fat-free mass; c fat mass; d basal metabolism. a was statistically analyzed using Pearson correlation analysis, and b, c, and d were statistically analyzed using Spearman correlation analysis. The graphs were created by GraphPad Prism

Fig. 5

Correlation analysis of blood bioindicators levels vs. ASMI across all subjects. a lymphocytes; b albumin; c TG, triglycerides; d TC, total cholesterol; e HDL-C, high-density lipoprotein cholesterol; f non-HDL-C, non-high-density lipoprotein cholesterol (this study refers to the sum of LDL-C and VLDL-C); g TSH, thyroid stimulating hormone; h FT4, free thyroxine; i FT3, free triiodothyronine. b, d, e, f, h, and i were statistically analyzed using Pearson correlation analysis, and a, c, and g were statistically analyzed using Spearman correlation analysis. The graphs were created by GraphPad Prism

Fig. 6

Multivariate binary logistic regression analysis of biological indicators levels vs. sarcopenia risk. BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; FT3, free triiodothyronine. The graph was created by GraphPad Prism