Abstract
Background
Frailty is a key element in healthy ageing in which muscle performance plays a main role. Beta‐hydroxy‐beta‐methylbutyrate (HMB) supplementation has shown favourable effects in modulating protein synthesis, improving muscle mass and function in interventional studies. Decreased age‐related endogenous HMB levels have been shown in previous studies. The aim of the present study is to assess whether there is an association between endogenous plasma HMB levels and frailty.
Methods
Data from 1290 subjects (56.98% women; mean ± standard deviation age 74.6 ± 5.95 years) from the Toledo Study for Healthy Aging were obtained. Participants had their frailty status qualified according to Fried's Frailty Phenotype (FFP) score and the Frailty Trait Scale in its 12‐domain version (FTS‐12). Plasma HMB levels were analysed by an ultrahigh‐performance liquid chromatography tandem mass spectrometry. Differences between groups (frail vs. non‐frail) were tested using Mann–Whitney U test, Kruskal–Wallis test and chi‐squared test. The association between HMB and frailty was assessed by multivariate linear and logistic regressions when frailty was analysed as continuous and binary, respectively. Models were adjusted by age, gender, comorbidity, body composition and protein intake.
Results
HMB levels were lower in those aged ≥75 years than in those aged 65–74 years, with an inverse linear relationship between age and HMB levels (β = −0.031; P = 0.018), mainly accounted by males (β = −0.062; P = 0.002). HMB levels were higher in men (0.238 ± 0.065 vs. 0.193 ± 0.051 ng/mL; P ≤ 0.001). HMB levels were significantly lower in frail than in non‐frail individuals: 0.204 ± 0.058 versus 0.217 ± 0.063 ng/dL (P = 0.001) according to the FFP and 0.203 ± 0.059 versus 0.219 ± 0.063 ng/mL (P < 0.001) according to FTS‐12. These differences showed a dose‐dependent profile when we compared them by quintiles of HMB (P for trend: 0.022; 0.012 and 0.0004, respectively, for FFP, FTS‐12 binary and FTS‐12 continuous). Variables associated with low HMB levels were body mass index, strength, exhaustion and weight loss. Frailty was associated with HMB levels in all the adjusted models, including the fully adjusted ones, no matter the tool used (odds ratio: 0.45 [0.26, 0.77] for FFP and 0.36 [0.20, 0.63] for FTS‐12 binary; β = −4.76 [−7.29, −2.23] for FTS‐12 score). This association was also observed when the analyses were done by quintiles, showing such association since Q4 (FFP), Q2 (FTS‐12 binary) and Q3 (FTS‐12 score). The associations were observed in the whole sample and in each gender.
Conclusions
There is an inverse association between HMB levels and frailty status. These findings support the design of targeted clinical trials to evaluate the effect of HMB supplementation in older frail people with low HMB levels.
Keywords: age, beta‐hydroxy‐beta‐methylbutyrate, community‐dwelling older people, frailty, HMB levels
Introduction
The relationship between frailty and nutritional status is important to understand, 1 , 2 as it could open the door to interventions aimed at either preventing or treating functional deterioration. The role of proteins and some of their components (amino acids and amino acid‐linked metabolites) is in focus given the role of muscle, 3 specifically sarcopenia, 4 , 5 in the development and progression of frailty. 6 , 7
Beta‐hydroxy‐beta‐methylbutyrate (HMB) is a metabolite derived from leucine, a branched‐chain amino acid intimately linked to muscle metabolism, which acts through the insulin‐like growth factor 1 (IGF‐1)/mammalian target of rapamycin (mTOR) pathway to promote protein anabolism in the muscle. 8 HMB supplementation has been evaluated in various populations, ranging from healthy adults 9 to malnourished individuals 10 , 11 , 12 and patients with chronic diseases, 13 , 14 , 15 and these studies have mostly demonstrated benefits on muscle mass and strength, 16 , 17 , 18 , 19 with heterogeneous results depending on the type of pathology, dose or supplementation regimen. 17
HMB is also endogenously produced in the body albeit at low levels. 20 A previous study has shown that endogenous HMB production varies with age and, in healthy adults, is positively associated with muscle mass and strength. 21 However, it is unknown whether endogenous HMB levels are associated with outcomes closely related to muscle loss and function. Taken as a whole, the age‐associated changes in plasma levels of endogenous HMB plus the relationship between them and muscle function raise the hypothesis that lower HMB levels could be associated with frailty in older adults.
The determination of plasma HMB is complex and laborious, a fact that could account for the scarcity of reports on endogenous plasma HMB levels in published studies. In addition, few studies have evaluated endogenous HMB levels in large populations and their association with clinical outcomes.
The present study has the main goal of assessing whether there is an association between endogenously produced HMB levels and frailty, determining the endogenous plasma levels of HMB in a large cohort of community‐dwelling Spanish adults over 65 years of age who have been appropriately characterized according to their frailty status.
Methods
Participants' data were taken from the Toledo Study for Healthy Aging (TSHA). TSHA is a prospective cohort study, designed to analyse determinants of frailty in community‐dwelling individuals older than 65 years living in the province of Toledo, Spain. Its general characteristics have been widely described in other works. 22 The study protocol was approved by the Clinical Research Ethics Committee of the Toledo Hospital Complex, Spain. All participants signed an informed consent prior to data acquisition. For the current analysis, we used the baseline data and samples for blood analysis from the second wave (2011–2013). The sample was initially composed of 1541 participants of the study who were phenotyped for frailty. They provided blood samples appropriately stored in the biobank of the study and had a determination of body composition through a dual‐energy X‐ray absorptiometry (DEXA) and an assessment of nutritional status and nutrient intake according to Mininutritional Assessment (MNA) 23 and Predimed Questionnaire (adherence to Mediterranean diet [AMD]). 24
From this initial sample, the participants with malnutrition (MNA < 17 points), body mass index (BMI) <18.5 kg/m2, severe liver disease (cirrhosis grade C Child), kidney disease (stages 3–5), sepsis, human immunodeficiency virus (HIV), rheumatoid arthritis, long‐lasting immobilization, poor‐controlled diabetes, active alcoholism and severe osteoporosis (defined as T score <−2.5 standard deviation [SD] in the neck of the femur, total hip or lumbar spine via DEXA) or taking drugs and/or supplements that could impact the quantitation of HMB or modify its baseline levels (oral nutritional supplements with HMB) were excluded. In addition, subjects using drugs like insulin and tamoxifen were not included in the analysis just in case there is an unknown impact on HMB metabolism causing potential bias. Insulin shares some parts of the metabolic pathway of HMB and could bias some of the effects we want to explore. In the case of tamoxifen, its anti‐oestrogenic role could mask the frailty status.
The final sample was composed of 1290 subjects (83.7% of the initial sample) (555 men and 735 women).
Study variables
Frailty
Frailty was assessed using the Fried's Frailty Phenotype (FFP) 25 and the Frailty Trait Scale in its 12‐domain version (FTS‐12). 26 According to the FFP, participants were classified as robust (no criteria), prefrail (1–2 criteria) or frail (≥3 criteria). In the case of FTS‐12, participants had their scores assessed, and after that, they were classified as non‐frail (≤45 points) or frail (>45 points).
Body composition
Total lean mass and body composition were determined using DEXA (Hologic, Discovery QDR Series, Bedford, MA, USA). DEXA scans were analysed using the software Physician's Viewer (Apex Systems Software, Version 3.1.2, Bedford, MA, USA).
Plasma levels of beta‐hydroxy‐beta‐methylbutyrate
Quantitation of HMB levels in the plasma samples (1.5‐mL aliquot from each subject) of the participants was conducted at Abbott Laboratories (Abbott Nutrition Research & Development, Discovery Technology, Camino de Purchil 68, Granada E‐18004, Spain), following the protocol described by Santos‐Fandila et al. 27 Samples were kept frozen at −80°C until subsequent biochemical data determination of HMB levels in plasma by ultrahigh‐performance liquid chromatography tandem mass spectrometry (UHPLC–MS/MS).
Adjustment variables
Adjusting variables were selected based on the following criteria: Gender and age as universal covariates, Charlson index to control by comorbidities, waist‐to‐hip ratio and total lean mass as markers of body composition, and several questions of the Predimed Questionnaire (Questions 5, 8–10 and 13) and MNA (Questions K1–K3) as markers of protein and wine intake.
Statistical analysis
Descriptive statistics are presented as mean (SD) for continuous variables and number (N, %) for discrete and categorical variables. For the purposes of the analysis, and considering the low prevalence of frailty when we used the FFP, we merged frail and prefrail participants. The cut‐off point for frailty when we used FTS‐12 was >45. Differences for continuous variables between groups (non‐frail vs. frail) were tested using Mann–Whitney U test and between quintiles through Kruskal–Wallis test. Chi‐squared test was used for the comparison between categorical variables.
The association between HMB and frailty was assessed by multivariate linear regressions and multivariate logistic regressions when frailty was analysed as continuous and binary, respectively. We used six nested regression models: (1) adjusted by Charlson index, age and gender; (2) adjusted by (1) plus waist‐to‐hip ratio; (3) adjusted by (2) plus uric acid, total bilirubin and calcium; (4) adjusted by (3) plus MNA protein intake (Questions K1–K3); (5) adjusted by (4) plus Predimed protein intake and Predimed wine consumption; and (6) adjusted by (5) plus total lean mass. All the analyses were computed using R for Windows Version 3.6.1.
Results
We analysed data from 1290 persons (female, 56.98%), with a mean ± SD age of 74.6 ± 5.95 years. Their demographic and anthropometric characteristics are shown in Table 1 (whole population and gender) and Table S1 (by quintiles). The mean ± SD value of plasma HMB was 0.213 ± 0.061 ng/mL, with significantly higher levels in men than in women (0.238 ± 0.065 vs. 0.193 ± 0.051 ng/mL; P ≤ 0.001). These levels were lower in people ≥75 years old than in those between 65 and 74 years (0.219 ± 0.063 vs. 0.207 ± 0.059 ng/mL; P = 0.021). Male participants account for these differences. In this subgroup (males), the level of HMB in younger participants (65–74 years) was higher than in the older ones (≥75 years) (0.247 ± 0.065 vs. 0.228 ± 0.064 ng/mL; P = 0.0002), while in females, there were no differences by age (0.195 ± 0.052 vs. 0.193 ± 0.051 ng/mL; P = 0.69). Moreover, we found an inverse linear relationship between age and HMB levels (β = −0.031; P = 0.018), a relationship that was shown only in males (β = −0.062; P = 0.002) but not in females (β = 0; P = 0.995).
Table 1.
Characteristics of the population of the study
| Total sample | Male | Female | P value | |
|---|---|---|---|---|
| N (%) | 1290 | 555 (43.02) | 735 (56.98) | |
| Age | 74.67 (5.95) | 74.45 (5.86) | 74.83 (6.02) | 0.3003 |
| HMB (ng/mL) | 0.213 (0.061) | 0.238 (0.065) | 0.193 (0.051) | 0.0000 |
| Comorbidities | ||||
| Diabetes | 228.00 (17.72) | 119.00 (21.44) | 109.00 (14.89) | 0.0023 |
| Hypertension | 821.00 (63.79) | 324.00 (58.38) | 497.00 (67.90) | 0.0004 |
| CVD | 106.00 (8.24) | 59.00 (10.63) | 47.00 (6.42) | 0.0065 |
| Heart disease (excluding IHD) | 137.00 (10.72) | 52.00 (9.49) | 85.00 (11.64) | 0.2178 |
| Stroke | 35.00 (2.72) | 18.00 (3.24) | 17.00 (2.32) | 0.3145 |
| Osteoarthritis | 756.00 (99.47) | 239.00 (99.58) | 517.00 (99.42) | 0.7766 |
| COPD | 42.00 (3.26) | 29.00 (5.23) | 13.00 (1.78) | 0.0006 |
| Falls (%) | 65.00 (5.06) | 31.00 (5.61) | 34.00 (4.64) | 0.4364 |
| Cognitive impairment | 244.00 (18.99) | 94.00 (16.97) | 150.00 (20.52) | 0.1150 |
| Charlson index | 0.93 (1.28) | 0.83 (1.22) | 1.01 (1.32) | 0.0270 |
| Body composition | ||||
| BMI | 29.25 (4.52) | 28.33 (3.64) | 29.95 (4.97) | 0.0000 |
| WHR | 0.90 (0.09) | 0.97 (0.07) | 0.85 (0.07) | 0.0000 |
| TLM (kg) | 17.29 (2.15) | 18.45 (1.88) | 16.41 (1.91) | 0.0000 |
Note: Values represent mean (SD). P value represents gender difference. In bold, P value < 0.05.
Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; CVD, cardiovascular disease; HMB, beta‐hydroxy‐beta‐methylbutyrate; IHD, ischaemic heart disease; TLM, total lean mass; WHR, waist‐to‐hip ratio.
The prevalence of frailty in our sample changed according to the criteria used, ranging from 2.9% for frailty and 28.22% for prefrailty according to FFP (31.12% when both groups were merged) to a maximum of 34.86% according to FTS‐12 (cut‐off point: 45) (Table 2 ). HMB levels were lower in people who were frail than in those non‐frails. Mean ± SD values were 0.204 ± 0.058 versus 0.217 ± 0.063 ng/mL (P = 0.001) when FFP was used and 0.203 ± 0.059 versus 0.219 ± 0.063 ng/mL (P < 0.001) for the FTS‐12 criteria. These differences showed a dose–effect relationship. When we assessed differences by quintiles of endogenous HMB levels, frailty was more frequent in the quintiles with the lower levels of plasma HMB, regardless of the tools to assess frailty. The results were consistent when analysed using a continuous (FTS‐12 score) or a binary (FFP and FTS‐12) approach (Table 2 ).
Table 2.
Prevalence of frailty and score of FTS‐12 by HMB quintiles
| Total sample | Quintile 1 | Quintile 2 | Quintile 3 | Quintile 4 | Quintile 5 | P for trend | |
|---|---|---|---|---|---|---|---|
| HMB cut‐off values (ng/mL) | <0.159 | 0.159–0.190 | 0.190–0.218 | 0.218–0.264 | >0.264 | ||
| N | 1290 | 258 | 258 | 257 | 259 | 258 | |
| Fried's Frailty Phenotype | |||||||
| N (%) | 0.0227 | ||||||
| Robust | 808 (68.88) | 155 (65.96) | 141 (63.51) | 158 (64.49) | 179 (74.27) | 175 (76.09) | |
| Prefrail | 331 (28.22) | 70 (29.79) | 74 (33.33) | 82 (33.47) | 55 (22.82) | 50 (21.74) | |
| Frail | 34 (2.90) | 10 (4.26) | 7 (3.15) | 5 (2.04) | 7 (2.90) | 5 (2.17) | |
| FTS‐12 a | |||||||
| FTS‐12 score | 40.45 (14.49) | 43.42 (14.74) | 41.45 (14.48) | 40.33 (14.38) | 38.63 (14.03) | 38.48 (14.36) | 0.0004 |
| FTS‐12 binary | 441 (34.86) | 116 (46.03) | 87 (35.08) | 84 (33.20) | 80 (31.13) | 74 (29.02) | 0.0126 |
Note: In bold, P value < 0.05.
Abbreviations: FTS‐12, Frailty Trait Scale in its 12‐domain version; HMB, beta‐hydroxy‐beta‐methylbutyrate.
The row ‘score’ shows the mean score in each quintile, while the row ‘binary’ shows the number (%) of people over the threshold (i.e., frail) in each quintile.
The variables included in each of the tools with a clear association with the low levels of HMB were predominantly those related to body composition and muscular function: BMI, strength, exhaustion or weight loss (Table S2 ).
The relationship between HMB levels and frailty status was also observed in the regression analyses. When we assessed the association between the frailty categories (binary: frail vs. non‐frail), we found an inverse association between HMB and frailty status, without attenuation in the adjusted models. In the case of FTS‐12, but not with the FFP, in the analysis using whole data, an interaction was observed with gender in the association between HMB levels and frailty. Therefore, a separate analysis was performed for each gender. In addition, we searched for analytical parameters that could account for this difference, finding that calcium, total bilirubin and uric acid were involved (M3) (Table 3 ). Accordingly, they were incorporated to the statistical adjusting models (M3–M6), showing that the association between HMB levels and frailty remains in male and females in the more adjusted models, both when using FTS‐12 binary or FTS‐12 score (Table 3 ).
Table 3.
Associations between HMB levels (log) and frailty according to different tools and models of adjustment
| Fried's Frailty Phenotype | ||||
|---|---|---|---|---|
| All OR (95% CI) | Gender–HMB interaction | Men OR (95% CI) | Women OR (95% CI) | |
| M1 | 0.55 (0.33, 0.90)* | >0.05 | NA | NA |
| M2 | 0.52 (0.31, 0.85)** | >0.05 | NA | NA |
| M3 | 0.46 (0.27, 0.78)** | >0.05 | NA | NA |
| M4 | 0.47 (0.27, 0.80)** | >0.05 | NA | NA |
| M5 | 0.45 (0.26, 0.77)** | >0.05 | NA | NA |
| M6 | 0.45 (0.26, 0.77)** | >0.05 | NA | NA |
| FTS‐12 binary | ||||
|---|---|---|---|---|
| All OR (95% CI) | Gender–HMB interaction | Men OR (95% CI) | Women OR (95% CI) | |
| M1 | 0.65 (0.40, 1.06) | <0.001 | 0.40 (0.18, 0.87)* | 0.92 (0.49, 1.72) |
| M2 | 0.52 (0.31, 0.85)* | <0.001 | 0.35 (0.16, 0.78)* | 0.70 (0.36, 1.33) |
| M3 | 0.32 (0.19, 0.56)*** | <0.001 | 0.25 (0.11, 0.62)** | 0.39 (0.19, 0.79)** |
| M4 | 0.35 (0.20, 0.62)*** | <0.001 | 0.30 (0.12, 0.73)** | 0.41 (0.20, 0.84)* |
| M5 | 0.35 (0.20, 0.61)*** | <0.001 | 0.29 (0.11, 0.72)** | 0.43 (0.21, 0.88)* |
| M6 | 0.36 (0.20, 0.63)*** | <0.001 | 0.28 (0.11, 0.71)** | 0.47 (0.23, 0.99)* |
| FTS‐12 score | ||||
|---|---|---|---|---|
| All β (95% CI) | Gender–HMB interaction | Men β (95% CI) | Women β (95% CI) | |
| M1 | −2.29 (−4.94, 0.35) | <0.001 | −4.79 (−8.57, −1.01)* | 0.07 (−3.62, 3.77) |
| M2 | −3.96 (−6.49, −1.43)** | <0.001 | −5.64 (−9.16, −2.13)** | −1.92 (−5.50, 1.67) |
| M3 | −5.98 (−8.54, −3.43)*** | <0.001 | −6.39 (−9.98, −2.79)** | −5.17 (−8.76, −1.57)** |
| M4 | −5.42 (−7.99, −2.85)*** | <0.001 | −6.06 (−9.72, −2.40)** | −4.66 (−8.24, −1.07)* |
| M5 | −5.35 (−7.92, −2.78)*** | <0.001 | −5.94 (−9.59, −2.29)** | −4.22 (−7.83, −0.60)* |
| M6 | −4.76 (−7.29, −2.23)*** | <0.001 | −5.90 (−9.56, −2.25)** | −2.94 (−6.44, 0.56) |
Note: M1 adjusted by Charlson index, gender and age. M2 adjusted by Charlson index, gender, age and WHR. M3 adjusted by M2 plus uric acid, bilirubin and calcium. M4 adjusted by M3 plus Questions K1–K3 of the MNA. M5 adjusted by M4 plus Questions 5, 8–10 and 13 of the Predimed Questionnaire. M6 adjusted by M5 plus log (total lean mass). Abbreviations: CI, confidence interval; FTS‐12, Frailty Trait Scale in its 12‐domain version; HMB, beta‐hydroxy‐beta‐methylbutyrate; MNA, Mininutritional Assessment; NA, not applicable; OR, odds ratio; WHR, waist‐to‐hip ratio.
P < 0.05.
P < 0.01.
P < 0.001.
Finally, we tested the relationships between HMB levels (by quintiles) and frailty (Table 4 ). FFP was found to be associated with the highest quintile (Q5) of HMB plasma levels in four of the five adjusted models and with the Q4 quintile in the highly adjusted models (M5 and M6), again without showing interaction according to gender. The association showed a protective effect of higher HMB plasma levels and frailty.
Table 4.
Association between HMB levels (log) and frailty by quintiles
| Fried's Frailty Phenotype | Q2 OR (95% CI) | Q3 OR (95% CI) | Q4 OR (95% CI) | Q5 OR (95% CI) | Gender–HMB interaction P value |
|---|---|---|---|---|---|
| M1 | 1.13 (0.75, 1.68) | 1.06 (0.70, 1.58) | 0.71 (0.46, 1.08) | 0.66 (0.42, 1.03) | >0.05 |
| M2 | 1.11 (0.74, 1.66) | 1.03 (0.69, 1.55) | 0.68 (0.44, 1.05) | 0.63 (0.40, 0.99)* | >0.05 |
| M3 | 1.06 (0.70, 1.60) | 0.98 (0.65, 1.49) | 0.65 (0.42, 1.01) | 0.58 (0.36, 0.93)* | >0.05 |
| M4 | 1.10 (0.72, 1.66) | 1.03 (0.67, 1.57) | 0.65 (0.42, 1.02) | 0.59 (0.37, 0.96)* | >0.05 |
| M5 | 1.07 (0.70, 1.62) | 1.02 (0.66, 1.56) | 0.63 (0.40, 0.99)* | 0.57 (0.35, 0.93)* | >0.05 |
| M6 | 1.11 (0.73, 1.69) | 1.03 (0.67, 1.58) | 0.63 (0.39, 0.99)* | 0.58 (0.35, 0.94)* | >0.05 |
| FTS‐12 binary | Q2 OR (95% CI) | Q3 OR (95% CI) | Q4 OR (95% CI) | Q5 OR (95% CI) | Gender–HMB interaction OR (95% CI) |
|---|---|---|---|---|---|
| M1 | 0.69 (0.47, 1.02) | 0.59 (0.40, 0.88)** | 0.67 (0.45, 1.01) | 0.73 (0.48, 1.11) | 0.85 (0.81, 0.90)*** |
| M2 | 0.64 (0.42, 0.95)* | 0.54 (0.36, 0.82)** | 0.60 (0.39, 0.92)* | 0.60 (0.39, 0.93)* | 0.71 (0.66, 0.76)*** |
| M3 | 0.56 (0.37, 0.85)** | 0.45 (0.29, 0.69)*** | 0.48 (0.31, 0.76)** | 0.41 (0.26, 0.66)*** | 0.69 (0.64, 0.75)*** |
| M4 | 0.58 (0.38, 0.88)* | 0.46 (0.30, 0.72)*** | 0.52 (0.33, 0.81)** | 0.45 (0.28, 0.72)*** | 0.69 (0.63, 0.74)*** |
| M5 | 0.56 (0.37, 0.86)** | 0.46 (0.30, 0.71)*** | 0.50 (0.32, 0.80)** | 0.44 (0.27, 0.71)*** | 0.69 (0.63, 0.75)*** |
| M6 | 0.57 (0.37, 0.88)* | 0.49 (0.31, 0.76)** | 0.51 (0.32, 0.82)** | 0.45 (0.28, 0.74)** | 0.61 (0.55, 0.67)*** |
| FTS‐12 score | Q2 β (95% CI) | Q3 β (95% CI) | Q4 β (95% CI) | Q5 β (95% CI) | Gender–HMB interaction β (95% CI) |
|---|---|---|---|---|---|
| M1 | −1.05 (−3.26, 1.16) | −2.31 (−4.54, −0.08)* | −2.53 (−4.79, −0.27)* | −1.67 (−4.01, 0.66) | −0.92 (−1.20, −0.63)*** |
| M2 | −1.62 (−3.73, 0.48) | −2.79 (−4.91, −0.67)* | −3.21 (−5.37, −1.06)** | −3.01 (−5.24, −0.78)** | −2.11 (−2.45, −1.78)*** |
| M3 | −1.97 (−4.04, 0.09) | −3.46 (−5.55, −1.36)** | −4.26 (−6.39, −2.12)*** | −4.58 (−6.83, −2.34)*** | −2.23 (−2.56, −1.89)*** |
| M4 | −1.74 (−3.79, 0.31) | −3.15 (−5.24, −1.07)** | −3.85 (−5.98, −1.71)*** | −4.09 (−6.34, −1.84)*** | −2.22 (−2.56, −1.89)*** |
| M5 | −1.80 (−3.85, 0.25) | −3.12 (−5.20, −1.03)** | −3.85 (−5.99, −1.71)*** | −4.06 (−6.32, −1.80)*** | −2.16 (−2.50, −1.81)*** |
| M6 | −1.43 (−3.45, 0.60) | −2.52 (−4.57, −0.46)* | −3.39 (−5.50, −1.28)** | −3.47 (−5.69, −1.25)** | −2.83 (−3.22, −2.43)*** |
Note: M1 adjusted by Charlson index, gender and age. M2 adjusted by Charlson index, gender, age and WHR. M3 adjusted by M2 plus uric acid, bilirubin and calcium. M4 adjusted by M3 plus Questions K1–K3 of the MNA. M5 adjusted by M4 plus Questions 5, 8–10 and 13 of the Predimed Questionnaire. M6 adjusted by M5 plus log (total lean mass). Abbreviations: CI, confidence interval; FTS‐12, Frailty Trait Scale in its 12‐domain version; HMB, beta‐hydroxy‐beta‐methylbutyrate; MNA, Mininutritional Assessment; OR, odds ratio; WHR, waist‐to‐hip ratio.
P < 0.05.
P < 0.01.
P < 0.001.
In the case of FTS‐12, both in the binary (frail/non‐frail) assessment and in the continuous (score) one, there was a strong association with HMB starting at its third quintile (Q3) of plasma level. Additionally, a weaker association was shown starting at Q2 in the binary approach. Again, these associations showed an interaction with gender. In both men and women, FTS‐12 was found to be associated with HMB levels, especially at the highest quintiles of HMB, and the trend was highly significant in the majority of the adjusted models for both men and women (Tables 4 , S3 and S4 ). Once again, these associations showed an inverse interaction between HMB levels and frailty status.
Discussion
This study is the first to examine the relationship between the basal plasma levels of endogenously produced HMB and frailty status in a large sample of older adults over 65 years of age (n = 1290). We have found an inverse relationship between the endogenous plasma level of HMB and frailty status, measured by two different frailty scales (FFP and FTS‐12). Previous work had looked at the association of endogenous HMB levels and age in a smaller population (n = 305). 28 Furthermore, it confirms that HMB levels are higher in men than in women. To date, the only work that had determined HMB levels covered an age range from childhood to adulthood, including a small number (n = 102) of subjects older than 65 years (mean age: 69.2 years). 28 In that study, they reported that endogenous HMB levels decline with age over the lifespan, with highest levels measured in children. Our results go a step further and show that HMB levels further decline with advanced age, being significantly lower in the oldest adults (age ≥75 years) compared with those between 65 and 74 years. Furthermore, we determined endogenous HMB levels to be higher in men than in women, in line with previous publications.
In our study, we used two different tools (FFP and FTS‐12) to assess frailty, both of them working under the conceptual paradigm of the phenotype. The rationale of this decision is based upon two main reasons: First, FTS‐12 has been shown to have better accuracy than the classical FFP to assess some risks 26 , 29 ; second, FTS‐12 allows to assess in a continuous manner the relationship between frailty status and plasma levels of HMB, thus increasing the power to detect such differences. In this regard, it must be noticed that the prevalence changed depending upon the tool used. This can be explained by the fact that FTS‐12 binary criteria embrace frailty and non‐frailty, as it differentiates frail from non‐frail, not assessing intermediate situations like prefrailty. 22 , 26 And this is why we have compared non‐frail (i.e., robust) with frail (including prefrail in the case of FFP) in our analyses.
The relationship between HMB and frailty is inverse. When we segmented the population by quintiles of endogenous HMB levels, frailty prevalence (by both FFP and FTS‐12) and frailty score (FTS‐12) decreased as HMB levels increased, in a dose‐dependent manner, and the items more closely related to HMB levels were those assessing or linked to muscle strength and performance (exhaustion and weight loss).
The inverse association between frailty and HMB levels observed in the bivariant comparison was maintained in the models of regression, disregarding the adjusting variables included in such models. In fact, we did not find modifications in odds ratio (OR) or β coefficients in the adjusted models, suggesting that the association is stable enough and non‐dependent upon the adjusting variables. In this same line, adjustment by protein intake and lean mass did not change significantly the strength of the association, suggesting that the association is independent from those two potential modulating factors. Similar findings were obtained in the analyses using HMB‐level quintiles. We found a strong inverse association between endogenous HMB levels and frailty, with some differences depending on the tools used. While this inverse association was observed in the highest two quintiles for the FFP, for FTS‐12, the association was observed even in the lower quintiles of HMB.
Finally, some differences regarding the interaction between HMB and gender were also found. While we did not find such interaction with the FFP, it was quite clear with FTS‐12. In fact, when we made a secondary analysis by gender using FTS‐12, although the association remained in both males and females, the strength of this association was higher in males than in females. Although both tools measure frailty inside the same conceptual framework (phenotype model), it is largely known that the concordance among the tools is modest, suggesting that each one of them measures different clinical subtypes of frailty. 30 This can account for the subtle differences in the findings, but taken as a whole, they support that the association between frailty and HMB levels is not dependent in its main part on the way of measuring frailty. 31 In this same regard, the effect of calcium, bilirubin and uric acid on the relationship between HMB level and frailty in women but not in men is difficult to explain. Although a protective effect of uric acid and frailty has been described, 32 we lack of explanation for the role of the other two factors involved, although addition of calcium plus vitamin D has been recently reported to improve the effects of HMB supplements on the skeletal muscle health in women, 33 suggesting a role for them in the association between HMB levels and function.
The main clinical consequence of our findings is that we have shown that the level of HMB is inversely linked to frailty status in a dose‐dependent manner, mainly with the parameters of frailty linked to body composition and muscular function: BMI, strength, exhaustion or weight loss. These results imply that when endogenous levels of HMB fall below a certain threshold, there is an association with frailty and weakness, while levels over the threshold appear to be protective. Although the exact mechanism behind the observed decrease in endogenous HMB levels between older subjects is unknown, the association of HMB levels with frailty and other indicators of weakness could imply its roles as a surrogate marker of physical robustness in these older adults. 34 This is in line with known association of endogenous HMB levels with muscle mass and strength in older adults and clinical populations. 17 , 35 , 36 , 37 At the same time, these findings provide a rationale for new intervention studies assessing the effect of supplementing HMB in non‐robust people with low endogenous levels of plasma HMB on muscle outcomes and frailty status, thus complementing the body of evidence supporting the benefits of HMB supplementation in older adults. 9 , 35
The main limitation of the study is its cross‐sectional design that precludes to find strong causal relationships. Although we cannot firmly exclude inverse causation, it is very unlikely that frailty can produce decreased levels of HMB, while according to the currently accepted aetiological hypothesis, supporting the association between proteins/amino acids deficits and frailty 3 , 6 , 38 supports the option for low levels of HMB as a contributing factor to frailty. Another potential limitation is the way we have assessed protein intake, which is indirect. However, the consistency of the findings when we included MNA or AMD in the models provided support to our findings and no patient suffered from malnutrition according to the Mini Nutritional Assessment Short‐Form (MNA‐SF) criteria.
The strengths of the study rely on the big sample size, the homogeneity of the functional assessment, using two validated tools, making it unlikely that the findings are tool related, the adjustment by the majority of the potential sources of bias and the sensitivity analysis by gender, taking into account the differences in the HMB levels observed between males and females. Moreover, the study has been done in older people living in the community, and not in a clinical setting—population that tend to have many confounding factors that could bias—improving the external validity of our findings.
In conclusion, HMB endogenous levels of frailty are inversely associated with prevalent frailty. Older frail individuals have lower levels of plasmatic HMB than non‐frail older adults. Although plasma levels differ by gender, this association is shown in both males and females, being stronger in the first ones, and does not depend upon the way of assessing frailty and the levels of HMB correlated with the markers of weakness (strength, BMI, exhaustion and weight loss) contained in the frailty assessment tools used. These findings open new opportunities for further investigating HMB as a potential blood biomarker of physical robustness and offer the opportunity for designing clinical trials to evaluate the effect of HMB supplementation in best targeted populations.
Funding
This work was supported by the Spanish Ministry of Science and Innovation and Ministry of Health, cofinanced by the European Regional Development Fund (Fondo Europeo de Desarrollo Regional [FEDER]; RD120001/0043) and Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable (CIBERFES; CB16/10/00464), and a Collaborative Study Agreement (February 2019) grant from Abbott Laboratories.
Conflict of interest statement
All authors declare no conflicts of interest.
Ethics statement
The authors comply with the ethical guidelines for authorship and publishing in the Journal of Cachexia, Sarcopenia and Muscle. 39 All participants gave informed, written consent prior to their inclusion in the study. The study was approved by the ethics committees of Hospital Virgen del Valle, Toledo, Spain, and Hospital Universitario de Getafe, Madrid, Spain. It has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.
Supporting information
Table S1. Characteristics of the population of the study by HMB quintiles.
Table S2. Association between frailty criteria and HMB quintiles.
Table S3. Regression models in males by HMB level quintiles when FTS‐12 was used to assess frailty.
Table S4. Regression models in females by HMB level quintiles when FTS‐12 was used to assess frailty.
Molina‐Baena B., Carnicero J. A., Pereira S. L., García‐García F. J., Santos‐Fandila A., Cabrera R. R., et al (2023) Association between endogenous plasma beta‐hydroxy‐beta‐methylbutyrate levels and frailty in community‐dwelling older people, Journal of Cachexia, Sarcopenia and Muscle, doi: 10.1002/jcsm.13394
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Supplementary Materials
Table S1. Characteristics of the population of the study by HMB quintiles.
Table S2. Association between frailty criteria and HMB quintiles.
Table S3. Regression models in males by HMB level quintiles when FTS‐12 was used to assess frailty.
Table S4. Regression models in females by HMB level quintiles when FTS‐12 was used to assess frailty.