Home Medicine Serum metabolic alterations after two weeks of step-reduction and following four weeks of exercise rehabilitation in older adults: a secondary analysis of the ENDURE randomised controlled trial
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Serum metabolic alterations after two weeks of step-reduction and following four weeks of exercise rehabilitation in older adults: a secondary analysis of the ENDURE randomised controlled trial

  • Eija K. Laakkonen ORCID logo EMAIL logo , Jari E. Karppinen ORCID logo , Ulla-Maria Sahinaho , Jari A. Laukkanen ORCID logo , Heikki Peltonen ORCID logo , Mika Ala-Korpela ORCID logo , Maarit Lehti ORCID logo and Simon Walker ORCID logo
Published/Copyright: December 4, 2025
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Translational Exercise Biomedicine
From the journal Translational Exercise Biomedicine Volume 2 Issue 4

Abstract

Objectives

This study examined the effects of step-reduction and subsequent step-recovery and exercise rehabilitation on systemic metabolism in older adults.

Methods

Participants were 66 eligible participants from the ENDURE randomised controlled trial allocated to an intervention group (n=32; 25 % male) or control group (n=34; 21 % male). The intervention group was instructed to limit their daily steps to a maximum of 2000 for two weeks (Period I), followed by a four-week exercise rehabilitation program (Period II) involving twice-weekly sessions of whole-body resistance and bicycle ergometer-based endurance training. Fasting blood samples were collected at baseline, after Period I, and after Period II. Systemic metabolism was assessed using high-throughput proton nuclear magnetic resonance spectroscopy. Data were normalised using Box-Cox transformation and analysed with linear mixed-effects models including random intercepts.

Results

Period I and Period II had largely opposing effects on systemic metabolism. For instance, compared to the control group, Period one led to increases in VLDL-phospholipids (0.54 SD, P = 0.005), VLDL-cholesterols (0.41 SD, P = 0.012) and VLDL-triglycerides (0.79 SD, P = 0.002), and decreases in HDL-phospholipids (−0.31 SD, P = 0.037) and HDL-cholesterols (−0.47 SD, P = 0.011), alongside an increase in HDL-triglycerides (0.64 SD, P = 0.011). These changes reversed during Period II. Glycoprotein acetylation biomarker GlycA levels were unaffected by either intervention.

Conclusions

These findings suggest that short-term inactivity does not markedly influence the inflammatory state but adversely affects lipoprotein metabolism and glycolytic pathways; however, these changes are reversible through the resumption of physical activity.

Keywords: low physical activity; exercise rehabilitation; lipoprotein particles; triglyceride metabolism; GlycA

Introduction

A lack of understanding of the potentially harmful health effects of short-term physical inactivity persists, although epidemiological studies have consistently demonstrated that prolonged low levels of physical activity contribute to the economic and individual burden of chronic diseases and premature mortality [1], 2]. Experimental models, including complete immobilisation and short-term bed rest, have shown that after such periods, older adults need intensive rehabilitation to recover and regain their previous level of functional capacity, which they may not always achieve [3], 4]. While these models simulate real-life situations of acute severe illness or trauma, they fail to capture the impact of more common, less severe reductions in activity. For example, in situations where older individuals are not completely immobile but considerably reduce their activity levels, such as during bad weather, mild personal health concerns, caregiving responsibilities, or difficulty leaving home alone.

An ecologically more representative experimental model is the short-term step-reduction approach, first introduced in 2008 by Olsen and co-workers to study how two to three weeks of reduced physical activity affected insulin sensitivity and related metabolism in young men [5]. Since then, several studies have used this model, as reviewed by Sarto et al. [6]. Of the 24 studies reviewed, 10 included older adults [7], [8], [9], [10], [11], [12], [13], [14], [15], [16]. To the authors’ knowledge, no additional step-reduction studies with older adults have been published other than our ENDURE study, a two-arm, parallel-group randomised controlled trial (RCT) [17]. The findings from these studies consistently indicate that two weeks of step-reduction have metabolic consequences for older adults. It impairs glucose clearance and insulin sensitivity (reported in six out of eight articles that studied these outcomes), but the effects on body composition and performance have been less consistent, and only two studies investigated and reported heightened systemic inflammation. The limited attention to inflammation in step-reduction studies is somewhat surprising, given the growing evidence that low physical activity contributes to elevated systemic inflammation in older adults [18]. Regular physical activity, in turn, is associated with reduced levels of pro-inflammatory markers [19], 20] such as CRP and IL-6 as well as a glycoprotein acetylation biomarker GlycA – a composite biomarker of systemic inflammation [21]. To our knowledge, GlycA responses have not been investigated in step-reduction studies.

In addition to impairments of glucose metabolism, step-reduction is likely to impair lipid metabolism. Nevertheless, our ENDURE-RCT study is thus far the only one to report changes in lipoprotein cholesterol concentrations following step-reduction [17]. Using standard clinical serum assessments, we observed a reduction in high-density lipoprotein cholesterol (HDL-C) in the intervention group, which returned to baseline levels during the exercise rehabilitation period [17]. While no notable changes in HDL-C occurred in the control group, between-group differences remained unclear (p-Value=0.065). The scientific consensus, although earlier studies are not completely uniform on the matter, begins to indicate that progressive aerobic exercise training shifts the systemic metabolic profile towards a higher HDL particle number, larger diameter of the particles, and higher HDL-C concentrations [22]. Quite often, concomitant reductions in total cholesterol and triglyceride (TG) levels and sometimes also changes in ApoB-containing lipoproteins have been reported, as summarised by Franszyk and co-workers [22]. Therefore, it is logical to assume that reduced aerobic activity, such as drastic step-reduction, would result in detrimental changes in these lipid parameters. However, to our knowledge, this has not been previously investigated in older adults.

In the present study, we subjected serum samples obtained in the ENDURE-RCT to targeted nuclear magnetic resonance spectroscopy (NMR), aiming to characterise the effects of step-reduction on markers of systemic metabolism and inflammation and to determine whether the effects are reversible following step-recovery and a four-week exercise rehabilitation program. We hypothesise that step-reduction will lead to negative trends in lipoproteins, other metabolic measures, and GlycA, but the subsequent exercise rehabilitation period will be able to reverse them. The summary of this article is presented in Figure 1.

Figure 1: Graphical representation of this study. Key points: (1) Short-term physical inactivity disrupts metabolic balance: Two weeks of step-reduction in older adults caused adverse changes in lipoprotein metabolism, with increased VLDL and decreased HDL, and deterioration in glycolytic and amino acid pathways, while inflammatory status (GlycA) remained stable. (2) The observed metabolic changes were reversible: A four-week exercise rehabilitation program effectively restored lipoprotein and energy metabolism profiles, showing that inactivity-induced disruptions can be mitigated by resuming physical activity. (3) Public health implication is reassurance for older adults: Brief periods of reduced activity do not cause permanent harm if followed by recovery of activity. Figure created with BioRender.
Figure 1:

Graphical representation of this study. Key points: (1) Short-term physical inactivity disrupts metabolic balance: Two weeks of step-reduction in older adults caused adverse changes in lipoprotein metabolism, with increased VLDL and decreased HDL, and deterioration in glycolytic and amino acid pathways, while inflammatory status (GlycA) remained stable. (2) The observed metabolic changes were reversible: A four-week exercise rehabilitation program effectively restored lipoprotein and energy metabolism profiles, showing that inactivity-induced disruptions can be mitigated by resuming physical activity. (3) Public health implication is reassurance for older adults: Brief periods of reduced activity do not cause permanent harm if followed by recovery of activity. Figure created with BioRender.

Materials and methods

Study design and randomisation

This study was a secondary analysis of a two-arm, parallel-group randomised controlled trial (ENDURE-RCT, NCT04997447) [17]. The primary outcome of the trial was leg lean mass change along the fitness variables (maximal strength, walking economy, functional capacity, and total body composition). The study was conducted from September to December 2021 according to the Declaration of Helsinki and was approved by the Ethics Committee of the Central Finland Health Care District (3U/2021). All participants provided written informed consent. Specific details regarding recruitment, sample size estimation, and inclusion/exclusion criteria are reported in the primary study [17]. Shortly, participants needed to be community-dwelling, non-smoking 70–80 year-old men or women without severe obesity (body mass index 20–35 kg/m2). In other words, participants were relatively healthy and well-functioning for their age. Their habitual physical activity resulted in over 5,000 steps per day, as assessed during a 5 days pre-intervention monitoring period using a hip-worn accelerometer. None of them were active masters athletes.

Randomisation was performed by allocating accepted participants into two groups using a computer-generated random number sequence in a 1:1 ratio. The primary investigator, blinded to participant codes, then randomly assigned participants to the intervention and control groups.

Interventions

While the control group continued to live as normal, the intervention group modified their physical activity levels throughout the study period. All participants were advised to maintain their normal dietary intake.

Period I: Step-reduction period. During the two-week step-reduction period, participants were instructed to limit their daily steps to a maximum of 2000. Daily steps were tracked by an accelerometer (UKK RM 42) and were calculated with ActiGaph Actilife software (ActiGraph LLC, Florida, USA). To provide visual feedback and record anomalies, the intervention group participants were also provided with pedometers (Omron Walking Style One, HJ-152R-S) and an activity diary to record any anomalies, such as removing the accelerometers during the day.

Period II: Exercise rehabilitation period. During the four-week exercise rehabilitation period, step restrictions were removed. Additionally, participants underwent a supervised, progressive exercise intervention consisting of whole-body resistance training twice per week and bicycle ergometer-based endurance training twice per week on separate days. Exercise sessions took place in the university’s facilities on Mondays, Tuesdays, Thursdays, and Fridays. Specific details of the exercise intervention have been published previously [17].

Outcomes

Outcomes were assessed at baseline, after Period I, and after Period II. In the intervention group, the Period II assessments were conducted 3–5 days after the last exercise rehabilitation session.

Blood sampling procedures

On the morning of an overnight fast (≥12 h), participants arrived at the laboratory at a fixed time (07:00–9:30), consistent throughout the study. Venous blood samples were drawn from the antecubital vein into 6 mL serum tubes (Vacuette tube, Greiner Bio-One Ltd., Austria). Samples stood at room temperature for 30 min before centrifugation at 3,600 rpm (2,245 rcf, Heraeus Megafuge 1.0 R, Heraeus Holding GmbH, Hanau, Germany) for 15 min at 20 °C. Aliquots of 0.5 mL were stored at −80 °C. For metabolomics analyses, frozen samples from all trial timepoints were shipped together.

Serum metabolic measures

A high-throughput proton nuclear magnetic resonance (1H-NMR) spectroscopy platform (Nightingale Health Ltd, Helsinki, Finland, biomarker quantification version 2020) was used to provide 250 metabolic measures, including serum lipid and lipoprotein concentrations or their ratios and numerous low-molecular-weight metabolites [23]. Regarding lipoprotein-related measures, we focused on size and concentration measures, including circulating lipoproteins, their size-based subclasses, and lipid concentrations, as well as the concentrations and ratio of apolipoprotein B (ApoB) and A-I (ApoA-I) and excluded all lipoprotein particle lipid composition measures from the analysis. We also included fatty acid concentrations and their ratios to total fatty acids, glycolysis-related metabolites, amino acids, ketone bodies and the composite inflammation marker GlycA. This provided 180 measures as outcomes for this study.

Data processing

The dataset was nearly complete, with only one participant in the control group and three in the intervention group missing 3-hydroxybutyrate values at Period II. Additionally, two participants in the control group had zero 3-hydroxybutyrate levels either at baseline or at Period II. We did not impute the missing values, as the cause of absence – whether due to analytical issues or low concentrations – could not be determined. However, we replaced the zero values with half of the minimum 3-hydroxybutyrate concentration to facilitate statistical testing.

NMR concentration measures at Period II were consistently higher compared to earlier timepoints. We verified this finding by comparing five of the measures (total triglycerides, total cholesterol, low-density lipoprotein (LDL) cholesterol, HDL cholesterol, and glucose) to their counterparts from clinical chemistry assessed in our laboratory (Indiko Plus, Thermo Fisher Scientific Inc., Vantaa, Finland). Additionally, we noted a slightly larger variation between measurement methods at Period II compared to baseline or Period I. This elevation in metabolic measures’ concentrations was observed in both the intervention and control groups. Although this measurement error did not necessarily influence our ability to analyse intervention effects, it prevented us from analysing within-group changes. Consequently, we calibrated the measurements at Period II using a similar approach to Mäkinen and colleagues [24]. First, we determined correction factors separately for each concentration measure using the control group data from baseline and Period I with the equation:

correction factor = exp log baseline + Period  I / 2   –   log Period II .

We then applied these correction factors to both the intervention and control group data from Period II. In essence, we generated a correction factor that aligned the control group data from Period II with the levels observed in earlier timepoints, and we then applied this factor to adjust the Period II data in the intervention group. This strategy bases on the assumption that metabolic measures’ concentrations remained stable in the control group during the study period. The data correction was able to bring the NMR and clinical chemistry measures to similar levels without affecting the between-participant variation.

Statistical analysis

We performed the statistical analyses using R v. 4.3.1. We present summary statistics as means ± standard deviations (SD). Because metabolic measures often exhibit skewness and yield residuals that violate regression assumptions, we applied the Box-Cox transformation to normalise their distributions (MASS package [25]). This method identifies a lambda parameter for each variable, optimising its distribution as close to normal as possible. To improve comparability between metabolic measures with varying units and concentrations, we scaled all measures to their baseline means and SDs.

We performed intention-to-treat (ITT) and per-protocol (PP) analyses using linear mixed-effects models with random intercepts (nlme package [26]). In these models, the outcomes were the metabolic measures, with group, time, group × time interaction and baseline level of metabolic measure as fixed effects, and participant identification as a random effect. We included metabolic measures’ baseline levels to minimise the impact of residual baseline differences. We also performed analyses adjusted for BMI and sex (data not shown), but as expected, these adjustments did not alter the results, given the baseline balance between groups.

We also conducted exploratory analyses using multiple linear regression to examine whether changes in step counts were associated with changes in metabolic measures during Periods I and II. The models included either baseline levels of metabolic measures or period I metabolic levels as covariates, respectively.

To account for multiple testing, we conducted a principal component analysis as recommended by Würtz and colleagues [27]. In the metabolic measures’ dataset, 12 principal components explained 95 % of the variation. Therefore, P <0.05/12 ≈ 0.004 indicates findings with stronger statistical support. In the presentation of results, findings with 0.004 ≤ P <0.05 are reported as suggestive and should be interpreted with caution.

Results

As previously published by Walker et al. [17], 78 individuals expressed interest and were screened for participation, 66 met eligibility criteria and were randomised to the intervention group (n=32; 8 males, 24 females) or the control group (n=34; 7 males, 27 females). The baseline characteristics were similar between the groups, including age (72.5 ± 2.6 vs. 72.7 ± 2.7 years), body mass index (25.9 ± 3.5 vs. 26.1 ± 3.3 kg/m2), 5-times sit-to-stand test (8.5 ± 1.0 vs. 8.3 ± 1.4 s) and daily steps (8,718 ± 3,815 vs. 8287 ± 2,907). All participants were non-smokers and metabolically relatively healthy older adults, with both groups including similarly low numbers of users of statins, antihyperglycemic drugs, beta-blockers, calcium channel blockers, angiotensin II receptor blockers or anticoagulants (n=1 to 6).

During Period I, 12 participants adhered to the instructed 2000 steps daily limit (daily steps: 1,597 ± 316) while 17 participants exceeded the limit (2,697 ± 568). Despite non-compliance, both subgroups reduced their activity compared to the control group (8,369 ± 3,627), whose daily steps remained stable. Five participants (intervention: 1 male, 2 females; control: 2 females) withdrew after Period I. Average adherence to the exercise rehabilitation was 97 % ± 5.7 %. One subject attended all 16 exercise sessions, one subject attended 12 sessions, and all others attended 14–15 sessions.

Following the principles of ITT reporting [28], with the modification of not imputing missing values for dropouts, we included all participants in the main ITT analysis. Additionally, we performed a PP analysis by excluding those participants who were non-compliant with the step-reduction protocol. The subsequent sections present the results of both ITT and PP analyses, focusing on selected metabolic measures that best characterise changes in the systemic metabolic profile. The complete results of the ITT analysis are presented in Supplemental Table 1 and of the PP analysis in Supplemental Table 2 for all metabolic measures.

Lipoprotein metabolism: step-reduction had limited effects on LDL, while VLDL parameters increased and most of the HDL parameters decreased

Table 1 presents the ITT analysis results for the total lipid and lipoprotein particle concentrations, as well as lipid concentrations within different lipoprotein particles and Figure 2A visualises the intervention effects for selected lipoprotein variables.

Table 1:

Intervention effect estimates of the intention-to-treat analysis for lipoprotein characteristics and total lipid amounts.

Change from baseline to period I: step-reductiona Change from period I to Period II: step-recovery & rehabilitationa
Control group Intervention group Group x time interaction Control group Intervention group Group x time interaction
Metabolite β SE p-Value β SE p-Value β SE p-Value β SE p-Value β SE p-Value β SE p-Value
Particle concentration (P). size. and apolipoprotein concentrations
Total −0.12 0.11 0.291 −0.28 0.11 0.017 −0.16 0.16 0.317 0.06 0.11 0.607 0.38 0.12 0.002 0.32 0.16 0.053
VLDL-P −0.06 0.11 0.622 0.13 0.12 0.290 0.18 0.16 0.270 −0.05 0.12 0.664 −0.05 0.12 0.710 0.00 0.17 0.978
LDL-P −0.07 0.08 0.372 0.01 0.08 0.874 0.08 0.11 0.462 0.02 0.08 0.777 −0.14 0.08 0.088 −0.16 0.11 0.151
HDL-P −0.11 0.11 0.304 −0.30 0.11 0.008 −0.19 0.16 0.231 0.06 0.11 0.612 0.42 0.12 <0.001 0.36 0.16 0.025
VLDL size 0.00 0.12 0.985 0.29 0.13 0.021 0.29 0.18 0.100 0.17 0.12 0.184 0.27 0.13 0.046 0.10 0.18 0.580
LDL size −0.10 0.14 0.482 0.21 0.15 0.159 0.31 0.20 0.134 0.01 0.14 0.942 −0.36 0.15 0.019 −0.37 0.21 0.078
HDL size 0.02 0.05 0.747 −0.11 0.05 0.040 −0.12 0.07 0.089 −0.02 0.05 0.636 0.12 0.05 0.022 0.15 0.07 0.046
ApoB −0.07 0.08 0.387 0.03 0.08 0.747 0.10 0.11 0.405 0.02 0.08 0.843 −0.12 0.08 0.158 −0.14 0.12 0.244
ApoA1 −0.07 0.09 0.408 −0.25 0.09 0.007 −0.18 0.13 0.169 0.03 0.09 0.737 0.36 0.10 <0.001 0.33 0.13 0.014
ApoB/A-I ratio −0.01 0.06 0.843 0.19 0.07 0.004 0.21 0.09 0.028 −0.07 0.06 0.287 −0.38 0.07 <0.001 −0.31 0.09 0.001
Phospholipids (PL)
Total −0.10 0.10 0.346 −0.08 0.10 0.430 0.01 0.15 0.928 0.02 0.10 0.824 0.16 0.11 0.153 0.13 0.15 0.373
VLDL-PL −0.04 0.11 0.728 0.17 0.12 0.158 0.21 0.16 0.209 −0.06 0.11 0.618 −0.06 0.12 0.635 0.00 0.17 0.997
LDL-PL −0.08 0.08 0.304 −0.05 0.08 0.540 0.03 0.12 0.782 0.04 0.08 0.625 −0.07 0.09 0.414 −0.11 0.12 0.353
HDL-PL −0.06 0.08 0.448 −0.22 0.08 0.009 −0.16 0.12 0.171 0.03 0.08 0.745 0.32 0.09 <0.001 0.30 0.12 0.014
Cholesterol (C)
Total −0.10 0.09 0.286 −0.08 0.09 0.399 0.02 0.13 0.890 0.05 0.09 0.616 0.05 0.10 0.630 0.00 0.13 0.996
VLDL-C −0.06 0.09 0.516 0.11 0.10 0.260 0.17 0.14 0.208 −0.01 0.10 0.954 −0.09 0.10 0.385 −0.08 0.14 0.553
LDL-C −0.09 0.08 0.276 −0.03 0.09 0.747 0.06 0.12 0.597 0.05 0.08 0.587 −0.06 0.09 0.517 −0.10 0.12 0.399
HDL-C −0.05 0.08 0.487 −0.28 0.08 0.001 −0.22 0.11 0.040 0.03 0.08 0.701 0.28 0.08 0.001 0.25 0.11 0.026
Esterified cholesterol (CE)
Total −0.10 0.09 0.278 −0.10 0.09 0.280 0.00 0.13 0.984 0.05 0.09 0.595 0.06 0.10 0.509 0.02 0.13 0.908
VLDL-CE −0.07 0.09 0.442 0.07 0.09 0.426 0.14 0.13 0.269 0.01 0.09 0.917 −0.10 0.09 0.291 −0.11 0.13 0.400
LDL-CE −0.09 0.08 0.279 −0.01 0.09 0.863 0.08 0.12 0.528 0.04 0.08 0.607 −0.06 0.09 0.533 −0.10 0.12 0.420
HDL-CE −0.05 0.08 0.470 −0.29 0.08 <0.001 −0.24 0.11 0.031 0.03 0.08 0.702 0.29 0.08 <0.001 0.26 0.11 0.020
Free cholesterol (FC)
Total −0.09 0.09 0.313 −0.02 0.09 0.835 0.07 0.13 0.580 0.04 0.09 0.687 0.00 0.09 0.984 −0.03 0.13 0.793
VLDL-FC −0.05 0.10 0.608 0.16 0.11 0.148 0.21 0.15 0.163 −0.03 0.11 0.788 −0.07 0.11 0.519 −0.04 0.15 0.776
LDL-FC −0.09 0.08 0.285 −0.07 0.09 0.449 0.02 0.12 0.840 0.05 0.08 0.566 −0.06 0.09 0.499 −0.11 0.12 0.376
HDL-FC −0.05 0.07 0.544 −0.21 0.08 0.008 −0.16 0.11 0.133 0.03 0.07 0.712 0.23 0.08 0.004 0.21 0.11 0.063
Triglycerides (TG)
Total −0.01 0.15 0.943 0.28 0.15 0.070 0.29 0.22 0.175 −0.18 0.15 0.230 −0.08 0.16 0.618 0.10 0.22 0.644
VLDL-TG 0.00 0.14 0.996 0.29 0.15 0.054 0.29 0.21 0.167 −0.19 0.15 0.194 −0.09 0.16 0.565 0.10 0.21 0.636
LDL-TG −0.02 0.12 0.846 −0.04 0.13 0.776 −0.01 0.18 0.945 −0.11 0.12 0.391 0.02 0.13 0.895 0.12 0.18 0.494
HDL-TG −0.03 0.14 0.817 0.18 0.15 0.216 0.21 0.20 0.294 −0.03 0.14 0.854 0.09 0.15 0.543 0.12 0.21 0.569
Figure 2: The intervention effects of the step-reduction (Period I) and step-recovery and exercise rehabilitation (Period II) on key metabolic measurements describing characteristics of lipoproteins. A) The standardized change estimates (expressed as standard deviation [SD] units) in the metabolic measure levels during Period I (left side) and Period II (right side) adjusted for baseline metabolic measure level. B) Individual data points allow visual comparisons between groups, time points, and compliers and non-compliers for VLDL-TG and HDL-C. Abbreviations: VLDL = very low-density lipoprotein, LDL = low-density lipoprotein, HDL = high-density lipoprotein, ApoB/A-I ratio = ratio between apolipoprotein B and apolipoprotein A–I. Symbols: A) open circle, non-statistically significant, closed circle P <0.05, B)***within group test P <0.001, #between group test P <0.05.
Figure 2:

The intervention effects of the step-reduction (Period I) and step-recovery and exercise rehabilitation (Period II) on key metabolic measurements describing characteristics of lipoproteins. A) The standardized change estimates (expressed as standard deviation [SD] units) in the metabolic measure levels during Period I (left side) and Period II (right side) adjusted for baseline metabolic measure level. B) Individual data points allow visual comparisons between groups, time points, and compliers and non-compliers for VLDL-TG and HDL-C. Abbreviations: VLDL = very low-density lipoprotein, LDL = low-density lipoprotein, HDL = high-density lipoprotein, ApoB/A-I ratio = ratio between apolipoprotein B and apolipoprotein A–I. Symbols: A) open circle, non-statistically significant, closed circle P <0.05, B)***within group test P <0.001, #between group test P <0.05.

Period I and Period II had largely opposing effects on lipoprotein particle characteristics. Among the apoB-containing particles, step-reduction mainly increased the number of very low-density lipoprotein (VLDL) particles, with this effect being more pronounced among the participants who adhered to the step-reduction target (Figure 2A and Supplemental Table 2). While the number of LDL particles remained relatively unchanged, their average size increased (PP: 0.67 SD, P = 0.011), consistent with the increase in VLDL particle size (PP: 0.66 SD, P = 0.002; robust for multiple testing correction). Conversely, the size (PP: −0.27 SD, P = 0.006) of apoA-I-containing HDL particles decreased during step-reduction, while the apoB/A-I ratio, which reflects the balance between the two main lipoprotein particle classes, increased (PP: 0.35 SD, P = 0.002; robust for multiple testing correction). Most of these changes were reversed during the step-recovery and exercise rehabilitation in Period II.

While the ITT analysis indicated no clear intervention effects on the total circulating amount of lipids, including phospholipids (PL), total cholesterol, esterified cholesterol (CE), and free cholesterol (FC), as well as TG (Table 1), the PP analysis revealed a notable (0.79 SD, P = 0.003; robust for multiple testing correction) increase in total TG during Period I (Figure 2A, Supplemental Table 2). When inspecting lipids within different lipoprotein particles, we observed no clear intervention effects for LDL-associated lipids. In contrast, the concentrations of lipids within VLDL particles systematically increased as a response to step-reduction (Figure 2A, Table 1). According to PP analysis, the increase was 0.54 SD (P = 0.005) for VLDL-PL, 0.41 SD (P = 0.012) for VLDL-C, and 0.79 SD (P = 0.002; robust for multiple testing correction) for VLDL-TG (Supplemental Table 2). The concentrations of lipids within HDL particles were also affected by the interventions (Figure 2A, Table 1). Compared to the control group, the intervention group experienced a 0.31 to 0.50 SD (for all P <0.05) decrease in HDL-PL, -C, -CE, and -FC and a 0.64 SD increase (P = 0.011) in HDL-TG during the step-reduction period and reversion of these parameters during the step-recovery and exercise rehabilitation period (Supplemental Table 2). In addition to total lipoprotein characteristics, we also analysed the corresponding data within size-based lipoprotein particle subclasses and observed very similar intervention effects (Supplemental Tables 1 and 2).

The observed intervention effects on VLDL-and HDL-associated cholesterols and triglycerides are novel in the context of step-reduction interventions. To transparently illustrate these changes and differences between compliers and non-compliers using raw data, we selected VLDL-TG and HDL-C as examples (Figure 2B). As is typical in metabolic measures quantitation data, substantial between- and within-individual variation among individuals existed. However, we did not observe such clustering to indicate that non-compliance would lead to overinterpretations, albeit it might mask some observable intervention effects.

Other metabolites: Step-reduction temporarily increased pyruvate levels and decreased glutamine, glycine, and citrate levels, but did not affect GlycA.

According to the ITT analyses, the interventions did not notably affect most lipid classes or fatty acid ratio variables (Figure 3A and Supplemental Table 2). However, the PP analysis indicated a shift in fatty acid profile from polyunsaturated to monounsaturated direction, which was not fully reversed during Period II.

Figure 3: The intervention effects of the step-reduction (Period I) and step-recovery and exercise rehabilitation (Period II) on (A) total lipids and fatty acid ratios, amino acids, glycolysis-related metabolites, ketone bodies, and other metabolites and (B) individual data points for citrate and total BCAAs. In A, metabolites are presented as standardized change estimates (expressed as standard deviation [SD] units) with 95 % confidence intervals (CI). Abbreviations: PUFA = polyunsaturated fatty acids, MUFA = monounsaturated fatty acids, SFA, saturated fatty acids, GlycA = global marker of glycoprotein acetylation, BCAAs = branched-chain amino acids. Symbols: A) open circle, non-statistically significant, closed circle statistically significant P <0.05, B) *within group test P <0.05, #between group test P <0.001.
Figure 3:

The intervention effects of the step-reduction (Period I) and step-recovery and exercise rehabilitation (Period II) on (A) total lipids and fatty acid ratios, amino acids, glycolysis-related metabolites, ketone bodies, and other metabolites and (B) individual data points for citrate and total BCAAs. In A, metabolites are presented as standardized change estimates (expressed as standard deviation [SD] units) with 95 % confidence intervals (CI). Abbreviations: PUFA = polyunsaturated fatty acids, MUFA = monounsaturated fatty acids, SFA, saturated fatty acids, GlycA = global marker of glycoprotein acetylation, BCAAs = branched-chain amino acids. Symbols: A) open circle, non-statistically significant, closed circle statistically significant P <0.05, B) *within group test P <0.05, #between group test P <0.001.

Of the amino acids, branched-chain amino acids (BCAAs) and aromatic amino acids (phenylalanine and tyrosine) remained stable throughout the study. However, glutamine (ITT: −0.56 SD, P = 0.048) and glycine (ITT: −0.29 SD, P = 0.044) decreased during Period I and then increased during Period II. Of the glycolysis-related metabolites, citrate first decreased (Period I, ITT: −0.77 SD, P = 0.006) and then increased (Period II, ITT: −0.63 SD, P = 0.003; robust for multiple testing correction), while the opposite was observed for pyruvate (ITT: 0.62 SD, P = 0.020 and −0.35 SD, P = 0.195, respectively). No substantial changes were observed for glucose or lactate.

Ketone bodies, creatine and GlycA did not significantly respond to either of the interventions. We selected citrate and total concentration of BCAAs as metabolites of interest for plots to provide individual-level observations of the raw data (Figure 3B).

Explorative analysis: decreases in step counts during the two-week step-reduction were associated with opposing changes in HDL and VLDL measures

We calculated change scores for median step counts as a decrease during Period I and an increase during Period II. Change scores were also calculated for all metabolic measures to assess whether larger step-reduction (Figure 4A) or step-recovery (Figure 4B) were associated with larger changes in variables of systemic metabolism (for complete data, see Supplemental Table 3). This analysis strengthens the patterns observed in earlier ITT and PP analyses: During the step-reduction period, decreases in daily steps were most strongly associated with changes in HDL and VLDL metabolism. Most HDL variables appear on the Volcano plot’s left side, indicating a greater decrease in HDL metrics with larger decreases in daily steps. In contrast, VLDL variables are plotted on the right, showing a greater increase in a variable with larger step count decreases (Figure 4A). Only a few significant associations were detected between Period II step count increases and changes in metabolic measures. However, increased step counts were moderately associated with increased glycine levels (Figure 4B). Individual-level data are presented for the most notably changed metabolic measures HDL-C (highest decrease with step decrease), VLDL-C (highest increase with step decrease) and glycine (highest increase with step increase).

Figure 4: Associations (standardized betas) between step-reduction at period I (A) and step increase at period II (B) with change in metabolic measures as volcano plots and the scatter plots presenting the most significant findings at the individual participant level. The models were adjusted for baseline step counts during Period I (A) and for Period I step counts during Period II (B).
Figure 4:

Associations (standardized betas) between step-reduction at period I (A) and step increase at period II (B) with change in metabolic measures as volcano plots and the scatter plots presenting the most significant findings at the individual participant level. The models were adjusted for baseline step counts during Period I (A) and for Period I step counts during Period II (B).

Discussion

Herein, we provide the first comprehensive picture of serum metabolic responses to a two-week step-reduction period and following four weeks of step-recovery and exercise rehabilitation in older adults. We verified the earlier finding of the ENDURE-RCT [17], obtained using clinical measurements: step-reduction leads to a temporary decrease in HDL-C levels, which is completely reversed by step-recovery and exercise rehabilitation. However, through NMR analysis, we expanded on these results to offer metabolic phenotyping of older adults’ responses to changes in physical activity levels. In general, we were able to confirm our hypothesis that unfavourable cardiometabolic adaptations occur in response to the step-reduction, but regaining activity results in positive adaptations, resetting the systemic metabolic profile back to baseline levels. However, in contrast to our hypothesis, we did not observe step-reduction nor regaining activity to influence GlycA, which is a well-accepted biomarker of systemic inflammation [29], indicating that no drastic changes in inflammatory processes occurred during interventions.

Our key findings indicate that step-reduction was primarily characterised by decreases in HDL particle and HDL-C concentrations, accompanied by increases in HDL-TG concentrations, and concomitant increases in VLDL particle and VLDL-lipid concentrations, thereby affecting the balance between VLDL- and HDL-mediated lipid metabolism. Given that increased steps through aerobic exercise have been shown to produce opposite trends in HDL and VLDL [22], our findings are not unexpected. However, to our knowledge, this is the first study to demonstrate these effects in older adults under conditions of step reduction. These observations are partially supported by Aadland and co-workers’ work, in which a panel of lipoprotein subclass particle concentrations assessed by NMR and standard clinical lipid assessments for total cholesterol, TG, LDL-C, and HDL-C were used to study associations between accelerometer-measured sedentary time and moderate-to-vigorous physical activity (MVPA) [30]. They found weak positive associations between sedentary time and higher concentrations of smaller VLDL-P, larger LDL-P, and TG. Conversely, MVPA was positively associated with a higher average HDL size and higher concentrations of larger HDL-P, ApoA-I, and HDL-C, supporting our observation of rebounds occurring in similar variables during the exercise rehabilitation period.

Although we could not directly measure energy expenditure or intake, it is plausible to assume that the step-reduction intervention substantially lowered energy expenditure without notably affecting energy intake, as participants were instructed to maintain their usual daily routines except for the step count. Thus, step-reduction with reduced energy expenditure presumably led to a positive energy balance and TG overflow. As a reflection of this TG overflow, we observed reductions in HDL particle counts and concentrations of HDL-PL and -C, particularly in L- and M-sized particles, alongside increases in HDL-TG across most particle sizes during step-reduction. These alterations were reversed upon the resumption of physical activity, indicating normalisation of energy balance during the step-recovery and exercise rehabilitation period.

In usual circumstances, over 90 % of all circulating lipoprotein particles are HDLs. S-HDLs are the most numerous. However, they nevertheless transport only about 35 % of all lipoprotein lipids [31]. VLDLs are central for TG disposal, carrying over 50 % of circulating TGs, while LDLs are typically cholesterol-enriched [31]. The role of VLDLs in TG clearance is particularly important in response to exercise [32]. It is well established that the lowering of TG levels observable 0.5–3 days post-exercise is VLDL-mediated and that this effect diminishes after detraining [32]. Therefore, repeated physical activity bouts are needed to maintain a high systemic VLDL-TG clearance rate. Our VLDL-related results are consistent with the theory of heightened demand for TG transport during low-activity periods. We observed increases in the number, size, and lipid concentrations of VLDL particles. The intervention did not notably affect LDL particles except for the increase in their size. It is logical to observe a stronger effect on VLDL than LDL because they have a shorter half-life in circulation (4–8 h vs. LDL’s 2.5 days), making their response to the probable positive energy balance occur more rapidly. The underlying mechanisms – whether increased VLDL synthesis, decreased clearance, or both – remain unclear. However, earlier studies point to a lack of physical activity leading to increased VLDL synthesis [33], without excluding other possibilities. HDL particles contribute to VLDL-mediated TG clearance by serving as recipients of VLDL-TGs, concomitantly losing their CEs through lipid exchange orchestrated by cholesteryl ester transfer protein (CETP) [34]. The interaction and lipid exchange between VLDL and HDL likely mechanistically explain our observations that step-reduction also leads to lower HDL particle number and HDL-C and a higher HDL-TG. This is because TG enrichment of HDL particles is known to enhance their catabolism [35]. Albeit simple measures for TG, LDL-C and HDL-C were sufficient to evaluate older adults’ clinical status and how it changed due to the step-reduction, our secondary analysis with more comprehensive lipoprotein analyses was necessary to understand the intervention effects on VLDL-HDL dynamics in TG transport and disposal.

Here, we did not observe intervention effects in fasting glucose levels. This is not surprising as systemic glucose homoeostasis is one of the most tightly controlled physiological systems [36]. However, earlier older persons’ step-reduction studies have observed deteriorations in glucose clearance and insulin sensitivity by using glucose tolerance tests [9], 11], 16] or hyperinsulinemic-euglycemic clamps [14]. Therefore, we cannot exclude the possibility of deteriorated glucose metabolism. Furthermore, the step-reduction protocol caused opposing changes in systemic pyruvate and citrate levels, suggesting alterations in glycolytic pathways. We also observed reductions and subsequent recovery in glutamine and glycine concentrations, but no other amino acid responses. Glutamine is the most abundant conditionally essential amino acid, estimated to be mainly released from tissues such as skeletal muscle (∼90 %), liver, and adipose tissue, into the blood circulation [37]. Its synthesis involves the transamination of BCAAs (leucine, isoleucine, and valine) to glutamate, which, with ammonia, is synthesised to glutamine catalysed by glutamine synthetase enzyme. Contradictorily, both strenuous exercise and inactivity have been shown to reduce plasma glutamine levels [38]. The hypothesis is that glutamine is needed to cover the excess energy demands and is also taken up and used by exercise-activated immune system cells. When energy expenditure is low, e.g., during bed rest periods, the tricarboxylic acid (TCA) cycle is downregulated concomitantly with reduced release of glutamine into the circulation. This agrees with simultaneously observing higher TG and pyruvate levels (the precursors of acetyl-CoA), and lower citrate levels (citrate is formed by citrate synthase in the first step of TCA, which combines acetyl-CoA with oxaloacetate) in response to step-reduction. Although we had no opportunity to assess enzyme activities, citrate synthase activity likely declined as observed earlier in young males subjected to seven days of step-reduction [39].

Systemic inflammation status is considered to be negatively affected by inactivity and obesity and positively influenced by higher physical activity and better fitness. Previous studies have shown 14 days of step-reduction to result in elevations in C-reactive protein (CRP), interleukin-6, and tumour necrosis factor α [9], 11], but GlycA has not been studied in this context. However, a Mendelian randomisation analysis concluded that sedentary behaviour assessed as greater time spent watching television is causally associated with higher GlycA levels, with stronger evidence than that obtained for CRP [40]. In a study using data from 14 endurance exercise interventions, each lasting at least 12 weeks, GlycA was consistently shown to be reduced by exercise, regardless of differences in age, sex, race and training volumes across the interventions [20]. GlycA was also associated with other markers of inflammation, such as CRP, interleukin-6, and fibrinogen. Based on these findings, we considered GlycA a robust marker of inflammation that could reflect the alterations in inflammatory status induced by changes in physical activity levels, whether upward or downward. However, in our study, neither step-reduction nor resumption activity influenced circulating GlycA levels. This may be interpreted positively, suggesting that no notable increase in inflammation occurred during the two-week inactivity period. Nevertheless, we cannot rule out the possibility that a mild elevation in inflammatory processes took place, but was not captured by GlycA assessment, which may better represent chronic rather than acute inflammatory states [41]. Furthermore, our four-week exercise rehabilitation program did not replicate previous findings of GlycA reduction following 5- to 6 month endurance training programs [20]. It is therefore possible that extending the training longer than one month would be required to elicit this beneficial effect.

We acknowledge that our study is not free of limitations. This is a secondary study of an RCT with leg lean mass as a primary outcome measure to base a priori sample size estimations. Therefore, the current study on systemic metabolic measures may have remained statistically underpowered. Although the statistical support was not particularly strong for most variables, i.e., p-Values were <0.05 but not <0.004, which would indicate stronger support accounting for multiple testing. Furthermore, group × time interactions of ITT analysis remain largely non-significant. Nevertheless, the observations are systematic and rational, which, together with the PP analysis, support our conclusions. We also report as a potential limitation the relatively short duration of the step-reduction period of two weeks, albeit similar or even shorter periods have been effective in earlier studies. Also, the present study did not control diet or take tissue samples. Furthermore, due to a low number of male participants, we were not able to study potential sex differences. We included explorative analysis using changes in daily steps as an indicator of decreased or increased physical activity and were able to confirm the patterns observed in earlier ITT and PP analyses. However, accounting only for steps underestimates the total amount of physical activity, thereby diluting intervention effects. This is particularly true during the step-recovery period because it also included supervised resistance and endurance training instead of being solely an add-back period for the steps. On the other hand, we were unable to determine the effect of merely regaining steps without other exercise rehabilitation.

Conclusions

To conclude, our key findings indicate that decreased physical activity is accompanied by opposing changes in HDL and VLDL metrics and deterioration in the TCA cycle, which underscores the importance of regular physical activity in maintaining a balanced energy metabolism. This study highlights the importance of staying physically active because even a short period of reduced activity induced potentially adverse cardiometabolic health-related changes in lipoprotein metabolism, glycolytic pathways, and amino acids. On the other hand, the composite inflammatory marker GlycA remained stable, suggesting no marked disruptions in inflammatory status. Another important finding was that the observed negative effects were transient. They were largely reversed during the step-recovery and exercise rehabilitation period, demonstrating that the temporary metabolic disruptions from physical inactivity can be mitigated by resuming regular exercise. This is an important and assuring finding for older adults. From the public health point of view, it is relieving to conclude that short-term step-reduction to <2000 steps per day does not seem to cause permanent harm that regaining activity would not offset.

Corresponding author: Associate Professor Eija K. Laakkonen, Faculty of Sport and Health Sciences, University of Jyväskylä, Rautpohjankatu 8, Jyväskylä, 40014, Finland, E-mail:
Laakkonen and Karppinen contributed equally to the manuscript and should be indicated as shared first authors.

Funding source: Research Council of Finland, Sydäntutkimussäätiö, Sigrid Juselius Foundation, Finnish Cultural Foundation

Award Identifier / Grant number: 330281

Award Identifier / Grant number: 341058

Award Identifier / Grant number: 350528

Award Identifier / Grant number: 357183

Award Identifier / Grant number: 00211177

Acknowledgments

We thank the staff at the Faculty of Sport and Health Sciences of the University of Jyväskylä for their invaluable assistance with data collection. We also thank all research volunteers for donating their time and effort.

  1. Research ethics: The study was conducted from September to December 2021 according to the Declaration of Helsinki and was approved by the Ethics Committee of the Central Finland Health Care District (3U/2021).

  2. Informed consent: All participants provided written informed consent.

  3. Author contributions: S.W., E.K.L. and J.E.K. conceived and designed research, S.W., U.-M.S., J.A.L., H.K., and M.L. performed experiments, J.E.K. analysed data, J.E.K., E.K.L., S.W., and M.A-K. interpreted results of experiments, J.E.K. prepared figures, E.K.L. draft manuscript, E.K.L., J.E.K., U-M.S., J.A.L., H.K., M.A-K., M.L. and S.W approved final version of manuscript.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflicts of interest: The authors state no conflict of interest. Eija K. Laakkonen serves as an Associate Editor for Translational Exercise Biomedicine, but was not involved in the handling, editorial review, or decision-making process for this manuscript. Jari A. Laukkanen serves as an editorial board member for Translational Exercise Biomedicine, but was not involved in the handling, editorial review, or decision making process for this manuscript.

  6. Research funding: This study was supported by the Research Council of Finland (#330281 to E.K.L, #350528 to S.W, #341058 to M.L and #357183 to M.A-K), by the Finnish Cultural Foundation (#00211177 to S.W), by the Sigrid Juselius Foundation (to M.A-K), and by the Finnish Foundation for Cardiovascular Research (to M.A-K).

  7. Data availability: The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.

  8. Clinical trial registration: This study was a secondary analysis of a two-arm, parallel-group randomised controlled trial (ENDURE-RCT, NCT04997447).

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Supplemenry Material

This article contains supplementary material (https://doi.org/10.1515/teb-2025-0028).

Received: 2025-08-28
Accepted: 2025-11-19
Published Online: 2025-12-04

© 2025 the author(s), published by De Gruyter on behalf of Shangai Jiao Tong University and Guangzhou Sport University

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/teb-2025-0028
Keywords for this article
low physical activity; exercise rehabilitation; lipoprotein particles; triglyceride metabolism; GlycA
Creative Commons
BY 4.0

Articles in the same Issue

  1. Frontmatter
  2. Section: Integrated exercise physiology, biology, and pathophysiology in health and disease
  3. The impact of exercise on the role of lipid droplets in maintaining health: a narrative review
  4. Effects of different interventions on microgravity-induced bone loss
  5. Section: Personalized and advanced exercise prescription for health and chronic diseases
  6. A therapeutic exercise program for adolescents engaged in gender diversity services: study protocol for a non-randomised clinical trial
  7. The role of exercise modality on psychological, behavioral, and fitness outcomes among individuals at risk of type 2 diabetes: preliminary evidence from the CHOICE pragmatic randomized trial
  8. Section: Physical activity/inactivity and health across the lifespan
  9. Serum metabolic alterations after two weeks of step-reduction and following four weeks of exercise rehabilitation in older adults: a secondary analysis of the ENDURE randomised controlled trial
  10. Calibration and evaluation of MET models for estimating energy expenditure using thigh and ankle-worn move 4 accelerometer
  11. Section: Sports medicine and movement science
  12. Muscular activity of hamstrings under attentional focus instructions: an electromyography based study
  13. Associations between anthropometric outcomes and fat percentage with physical performance in professional soccer players: a cutoff points approach
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