Abstract

   Molecular effects of lifestyle interventions are typically studied in a
   single tissue. Here, we perform a secondary analysis on the
   sex-specific effects of the Growing Old TOgether trial (GOTO, trial
   registration number GOT NL3301
   ([38]https://onderzoekmetmensen.nl/nl/trial/27183), [39]NL-OMON27183,
   primary outcomes have been previously reported in ref. ^[40]1), a
   moderate 13-week combined lifestyle intervention on the transcriptomes
   of postprandial blood, subcutaneous adipose tissue (SAT) and muscle
   tissue in healthy older adults, the overlap in effect between tissues
   and their relation to whole-body parameters of metabolic health. The
   GOTO intervention has virtually no effect on the postprandial blood
   transcriptome, while the SAT and muscle transcriptomes respond
   significantly. In SAT, pathways involved in HDL remodeling, O[2]/CO[2]
   exchange and signaling are overrepresented, while in muscle, collagen
   and extracellular matrix pathways are significantly overexpressed.
   Additionally, we find that the effects of the SAT transcriptome closest
   associates with gains in metabolic health. Lastly, in males, we
   identify a shared variation between the transcriptomes of the three
   tissues. We conclude that the GOTO intervention has a significant
   effect on metabolic and muscle fibre pathways in the SAT and muscle
   transcriptome, respectively. Aligning the response in the three tissues
   revealed a blood transcriptome component which may act as an integrated
   health marker for metabolic intervention effects across tissues.

   Subject terms: Lifestyle modification, Data integration, RNA, Applied
   mathematics
     __________________________________________________________________

   Molecular effects of lifestyle interventions are typically studied
   within one tissue, neglecting potential shared responses across
   tissues. Here, the authors show that subcutaneous adipose tissue RNA
   levels best capture health benefits of the intervention and identified
   joint effects among blood, subcutaneous adipose tissue and muscle
   tissue RNA levels.

Introduction

   Worldwide, the proportion of older people in our society is increasing,
   and with it the burden of age-related diseases on the quality of life
   and health economics^[41]2,[42]3. Improving metabolic health is
   considered key to improving vitality at older ages^[43]4,[44]5.
   Dietary, activity-based, or combined lifestyle interventions are known
   to improve culprit factors in human ageing such as metabolic health and
   immunity. Although effects are usually heterogeneous, the metabolic
   health can significantly be improved, even in middle and older
   age^[45]1,[46]6–[47]10.

   The effect of lifestyle interventions can be studied by traditional
   blood-based markers of metabolic health (including: BMI, fasting
   insulin, systolic blood pressure (SBP), HDL cholesterol, diastolic
   blood pressure (DBP), dual-energy X-ray absorptiometry (DEXA)-based
   whole-body fat%, trunk fat%)^[48]1,[49]11 and a range of omics based
   assays such as the metabolome^[50]1,[51]12–[52]14,
   proteome^[53]15,[54]16, epigenome^[55]8 and the
   transcriptome^[56]17–[57]27, of
   blood^[58]1,[59]12–[60]17,[61]19,[62]28,[63]29, gut^[64]15, adipose
   tissue^[65]18,[66]20,[67]25,[68]30,[69]31, and muscle
   tissue^[70]21,[71]22,[72]26,[73]27. Human intervention studies are
   usually small (n = 120–250)^[74]1,[75]29,[76]32–[77]34 and focus on one
   tissue only, mainly blood. Mostly, a strong effect is observed on the
   fasting blood proteome^[78]16 and metabolome^[79]1, but typically not
   on the fasting transcriptome, where the effects are milder and more
   variable^[80]17,[81]23,[82]35, except for rigorous lifestyle
   interventions^[83]29. The transcriptomes of adipose tissue and muscle
   do react respectively on separate dietary^[84]24 and physical
   activity^[85]21,[86]22,[87]26,[88]27 lifestyle interventions. In mice,
   combined lifestyle interventions were shown to affect the transcriptome
   of brown adipose tissue and skeletal muscle^[89]36.

   In humans, however, the simultaneous effect of combined lifestyle
   interventions on the transcriptome of blood, subcutaneous adipose
   tissue (SAT), and muscle was not investigated as yet. To understand how
   differences in omics profiles relate to changes over the years in
   health parameters, one often relies on population-based omics studies.
   However, such studies mainly rely on blood samples^[90]37,[91]38. We
   speculate that some of the transcriptomic changes in different tissues
   in response to interventions align due to the systemic nature of
   lifestyle interventions and that a part of changes in the blood
   transcriptome reflect responses in SAT and muscle and related health
   gains. Hereto, an integrative analysis of the SAT, muscle and blood
   transcriptomic response to such intervention is necessary.

   Integrative data analysis methods pool data from different datasets to
   jointly analyze them^[92]39. Joint and Independent Variation Explained
   (JIVE)^[93]40 is one such integrative data analysis method. JIVE
   decomposes the variation across the different datasets in three
   components: (1) the joint variation between the datasets, (2)
   dataset-specific variations, and (3) the residual noise for each
   dataset separately. The blood transcriptome response in mild
   interventions is usually minimal, JIVE however, may reveal a part that
   represents the joint response variation across tissues. We are
   interested whether the joint response captures the intervention effects
   on metabolic health stronger than the blood transcriptome alone.

   We applied the tissue-specific as well as the integrative data analysis
   approach on a 13-week combined physical activity and dietary lifestyle
   intervention trial in older adults; the Growing Old TOgether (GOTO)
   trial. The primary outcome of the GOTO trial was a reduction in fasting
   insulin along with general improvement in metabolic health, through a
   mild combined lifestyle intervention^[94]1. Within this trial the
   transcriptome of postprandial blood, SAT and muscle tissue before and
   after intervention has been measured. We report on genes whose
   expressions within each of the different tissues associated with
   whole-body health improvements across the intervention, and report on
   the observed joint transcriptional effect. We conclude that especially
   the SAT and muscle transcriptome respond to the intervention by HDL
   remodeling, cell signaling and CO[2]/O[2] exchange for SAT, and
   collagen/extracellular matrix pathways for muscle. Especially the SAT
   transcriptome in both sexes associates to beneficial changes in
   whole-body composition. The blood transcriptome does not generally
   respond to intervention or reflect health gain. Our integrative
   analysis of blood, however, revealed that the joint effect of the three
   tissues associated with the parameters of whole-body composition
   parameters. This blood transcriptome component may act as an integrated
   health marker for metabolic intervention effects across tissues.

Results

GOTO lifestyle intervention trial

   The Growing Old TOgether (GOTO) trial, was a 13-week combined lifestyle
   intervention with 164 participants, of whom 163 completed the trial.
   One participant withrew from the trial due to knee surgery. The primary
   outcome of the trial was a significant reduction in fasting insulin,
   these effects have been reported in ref. ^[95]1. The GOTO trial is
   completed.

Baseline characteristics of the GOTO participants were similar among subsets

   At baseline, the participants of the GOTO trial were non-diabetic
   overall healthy older adults, with a mean age of 63 for males and 62
   for females (Table [96]1). Some of the participants were overweight
   and/or had elevated blood pressure. Transcriptome data are available at
   baseline and post intervention for different sets of participants of
   the complete GOTO trial group, i.e.: (1) 57 participants (33 males, 24
   females) with transcriptome data of postprandial blood, SAT, and muscle
   samples, (2) 88 blood postprandial samples, containing 45 males and 43
   females, (3) 78 SAT postprandial samples, which contains 38 males and
   40 females, and 4) 82 muscle postprandial samples, 48 males and 34
   females (Supplementary Fig. [97]1).

Table 1.

   Baseline measurements of metabolic health marker measurements per
   subset selection
   Health marker, baseline mean (SD) [limits] Sex Complete GOTO dataset
   153 (75 male, 78 female) Overlapping blood, SAT and muscle samples 57
   (33 male, 24 female) Blood postprandial samples 88 (45 male, 43 female)
   SAT postprandial samples 78 (38 male, 40 female) Muscle postprandial
   samples 82 (48 male, 34 female)
   Age, years Male 63.95 (5.36) [50.03–74.33] 65.08 (5.65) [50.03–74.33]
   64.21 (5.68) [50.03–74.33] 64.86 (5.51) [50.03–74.33] 64.4 (5.5)
   [50.03–74.33]
   BMI, kg/m^2 Male 26.53 (1.95) [21.94–33.04] 26.58 (2.04) [21.94–32.11]
   26.59 (2) [22.03–32.11] 26.78 (2.19) [21.94–32.11] 26.6 (2.05)
   [22.03–32.11]
   Waist circumference Male 98.2 (6.47) [75.00–117.00] 99.27 (6.03)
   [75.00–112.00] 98.6 (6.39) [81.00–112.00] 99.58 (6.09) [75.00–112.00]
   98.9 (6.56) [82.00–112.00]
   Systolic blood pressure, mm Hg Male 86.22 (7.52) [66.50–107.50] 87.66
   (7.99) [66.50–107.00] 86.87 (7.58) [66.50–107.00] 87.74 (7.91)
   [66.50–107.00] 86.79 (7.64) [66.50–107.00]
   Diastolic blood pressure, mm Hg Male 141.02 (14.84) [114.00–182.50]
   142.95 (14.67) [114.00–182.50] 140.46 (15.13) [114.00–182.50] 142.36
   (14.44) [114.00–182.50] 140.95 (14.76) [114.00–182.50]
   Fasting insulin, mU/L Male 9.42 (4.06) [2.00–22.60] 9.39 (3.23)
   [2.91–20.00] 9.07 (3.24) [2.00–20.00] 9.69 (3.57) [2.00–20.00] 9.36
   (3.59) [3.43–20.00]
   Whole-body fat, % Male 26.5 (4.37) [14.83–38.06] 26.76 (4.64)
   [14.83–38.06] 26.25 (4.7) [16.14–38.06] 27.02 (4.71) [14.83–38.06] 26.5
   (4.77) [16.14–38.06]
   Trunk fat, % Male 28.17 (5.38) [11.97–39.91] 28.09 (5.33) [11.97–39.91]
   27.55 (5.68) [15.44–39.91] 28.42 (5.56) [11.97–39.91] 27.85 (5.84)
   [15.44–39.91]
   Fasting HDL cholesterol, mmol/L Male 1.36 (0.23) [0.81–1.92] 1.38
   (0.24) [0.81–1.92] 1.36 (0.24) [0.81–1.92] 1.38 (0.23) [0.81–1.92] 1.36
   (0.24) [0.81–1.92]
   Fasting HDL cholesterol size, nm Male 9.49 (0.14) [9.24–9.98] 9.49
   (0.15) [9.29–9.98] 9.49 (0.14) [9.29–9.98] 9.5 (0.14) [9.29–9.98] 9.49
   (0.14) [9.29–9.98]
   FRS Male 12.96 (1.54) [9.00–17.00] 13.21 (1.29) [9.00–17.00] 12.98
   (1.44) [9.00–17.00] 13.11 (1.35) [9.00–17.00] 13.04 (1.35) [9.00–17.00]
   Age, years Female 62.02 (6) [46.70–75.11] 61.83 (4.84) [48.41–75.11]
   62.74 (5.4) [48.41–69.25] 61.86 (5.27) [49.11–69.25] 61.99 (4.77)
   [49.11–69.25]
   BMI, kg/m^2 Female 27.04 (2.73) [21.04–33.73] 26.86 (2.68)
   [21.04–32.57] 26.67 (2.34) [21.04–33.55] 27.21 (2.68) [21.04–32.57]
   26.85 (2.66) [21.04–32.57]
   Waist circumference Female 93.36 (7.83) [72.00–112.00] 93.33 (7.2)
   [73.00–112.00] 93.14 (7.48) [74.00–112.00] 95.1 (7.28) [73.00–112.00]
   93.68 (7.35) [76.00–112.00]
   Systolic blood pressure, mm Hg Female 84.23 (8.6) [62.50–107.75] 85.18
   (9.14) [62.50–107.75] 84.07 (8.46) [71.00–107.75] 85.89 (8.47)
   [71.00–107.75] 84.66 (9.23) [71.50–107.75]
   Diastolic blood pressure, mm Hg Female 137.72 (17.27) [101.25–177.50]
   138.16 (17.06) [101.25–175.00] 136.05 (16.37) [107.50–175.00] 137.59
   (15.84) [107.50–175.00] 136.48 (15.96) [107.50–175.00]
   Fasting insulin, mU/L Female 8.35 (3.99) [2.00–22.50] 7.78 (3.2)
   [2.00–16.90] 7.57 (3.59) [2.00–22.50] 8.24 (4.12) [2.00–18.70] 7.69
   (3.25) [2.00–16.70]
   Whole-body fat, % Female 38.16 (4.39) [24.25–47.36] 37.34 (4.81)
   [24.25–45.51] 37.7 (4.24) [24.25–45.51] 37.85 (4.46) [24.25–45.51]
   37.19 (4.92) [24.25–45.51]
   Trunk fat, % Female 37.4 (5.55) [18.22–49.03] 36.93 (5.82)
   [20.17–49.03] 36.82 (5.18) [18.22–49.03] 37.3 (5.79) [18.22–49.03]
   36.72 (6.2) [20.17–49.03]
   Fasting HDL cholesterol, mmol/L Female 1.64 (0.28) [1.08–2.32] 1.64
   (0.27) [1.08–2.32] 1.68 (0.29) [1.08–2.32] 1.66 (0.26) [1.13–2.17] 1.64
   (0.26) [1.13–2.12]
   Fasting HDL cholesterol size, nm Female 9.62 (0.17) [9.31–10.06] 9.59
   (0.16) [9.33–10.06] 9.62 (0.17) [9.33–10.06] 9.61 (0.16) [9.34–10.06]
   9.62 (0.17) [9.34–10.02]
   FRS Female 14.88 (2.72) [7.00–21.00] 14.88 (2.58) [9.00–20.00] 14.91
   (2.56) [9.00–20.00] 14.8 (2.33) [9.00–20.00] 14.71 (2.3) [9.00–20.00]
   [98]Open in a new tab

   SD standard deviation, SAT subcutaneous adipose tissue, BMI body mass
   index, FRS Framingham Risk Score.

   Between the participants in the different subsets, there was only a
   slight variation in baseline measurements of the metabolic health
   markers (Table [99]1). There were differences between the baseline
   characteristics of males and females, especially in whole-body fat%,
   trunk fat%, HDL cholesterol level, which measurements were all higher
   in females than in males: 38.16%, 37.4% & 1.64 mmol/L versus 26.5%,
   27.17% & 1.36 mmol/L, respectively. Fasting insulin, the primary
   outcome of the GOTO trial, was higher at baseline in males than in
   females: 9.42 mU/L versus 8.35 mU/L. These differences in baseline
   metabolic health between the two sexes, along with earlier published
   results on different activity patterns and differences in the
   intervention effect on metabolic health between the
   sexes^[100]1,[101]41, prompted us to perform the analysis in a
   sex-stratified manner.

Health markers improve but improvement varies among subsets

   Participants compliant to the intervention overall improved their
   metabolic health, with for some parameters slight sex differences in
   magnitude of effect (Table [102]2). The direction of effect on the
   health parameters in the subsets of participants for which data in
   different tissues is available was the same, though there were some
   differences in significance level. For example, in males, the
   intervention only had a significant effect on HDL cholesterol in the
   group with muscle transcriptome measurements. While in females, HDL
   cholesterol size was significantly different in only the participants
   with SAT transcriptome measurements, and the Framingham Risk Score
   (FRS) was significantly different in the complete set of measurements.
   These differences in effect could be partially explained by slight
   baseline classical metabolic health marker level differences between
   the groups (Table [103]1). To circumvent the slight differences in
   intervention effect on the classical metabolic health markers between
   the different groups, the associations between the transcriptome and
   the classical metabolic health markers will only be calculated on the
   participants with transcriptome data available in all three tissues
   (Supplementary Fig. [104]1).

Table 2.

   Effect of the 13-week lifestyle intervention on metabolic health marker
   measurements differs per subset selection
   Complete GOTO dataset
   153 (75 male, 78 female) Overlapping blood, SAT and
   muscle samples 57 (33 male, 24 female) Blood postprandial samples 88
   (45 male, 43 female) SAT postprandial samples 78 (38 male, 40 female)
   Muscle postprandial samples 82 (48 male, 34 female)
   Health marker, mean (SD) Sex Delta (SD) P adjust Delta (SD) P adjust
   Delta (SD) P adjust Delta (SD) P adjust Delta (SD) P adjust
   BMI, kg/m^2 Male −1.09 (0.78) 2.4e-18* −1.02 (0.78) 1.8e-07* −1.15
   (0.79) 1.4e-11* −1.10 (0.77) 1.3e-09* −1.18 (0.78) 6.9e-13*
   Waist circumference Male −4.49 (5.26) 1.8e-09* −3.82 (4.97) 1.1e-03*
   −4.47 (5.29) 1.1e-05* −4.42 (5.10) 4.8e-05* −4.94 (5.41) 8.7e-07*
   Systolic blood pressure, mm Hg Male −3.25 (12.54) 2.8e-01 −3.80 (14.36)
   1.0e + 00 −2.52 (13.42) 1.0e + 00 −3.91 (14.14) 9.7e-01 −3.05 (13.53)
   1.0e + 00
   Diastolic blood pressure, mm Hg Male −0.75 (6.98) 1.0e + 00 −3.04
   (7.09) 1.9e-01 −2.27 (6.67) 2.7e-01 −3.09 (7.10) 1.1e-01 −2.32 (6.86)
   2.3e-01
   Fasting insulin, mU/L Male −0.31 (3.30) 1.0e + 00 0.14 (3.63) 1.0e + 00
   0.04 (3.62) 1.0e + 00 −0.11 (3.71) 1.0e + 00 −0.23 (3.72) 1.0e + 00
   Whole-body fat, % Male −1.60 (1.65) 1.2e-09* −1.17 (1.55) 1.5e-03*
   −1.43 (1.71) 8.2e-06* −0.92 (1.52) 9.3e-05* −1.77 (1.66) 4.8e-07*
   Trunk fat, % Male −2.47 (2.13) 3.2e-12* −1.96 (1.81) 3.3e-05* −2.26
   (2.13) 2.3e-07* −1.48 (1.74) 1.1e-06* −2.59 (2.04) 8.7e-09*
   Fasting HDL cholesterol, mmol/L Male 0.04 (0.14) 8.2e-02 0.04 (0.13)
   1.0e + 00 0.06 (0.14) 7.7e-02 0.05 (0.13) 3.4e-01 0.07 (0.14) 1.7e-02*
   Fasting HDL cholesterol size, nm Male 0.06 (0.09) 2.0e-06* 0.06 (0.08)
   9.9e-04* 0.08 (0.08) 2.0e-06* 0.06 (0.09) 4.6e-04* 0.08 (0.09) 1.3e-06*
   FRS Male −0.40 (1.22) 5.8e-02 −0.61 (1.37) 1.6e-01 −0.53 (1.25) 6.6e-02
   −0.58 (1.31) 9.7e-02 −0.54 (1.24) 3.9e-02*
   BMI, kg/m^2 Female −1.23 (0.82) 1.4e-20* −1.26 (0.72) 1.4e-07* −1.27
   (0.64) 2.7e-15* −1.35 (0.73) 3.0e-13* −1.36 (0.74) 2.5e-11*
   Waist circumference Female −4.33 (5.57) 1.4e-08* −4.71 (5.74) 5.4e-03*
   −4.70 (5.30) 7.5e-06* −4.58 (5.31) 3.0e-05* −5.21 (5.32) 2.3e-05*
   Systolic blood pressure, mm Hg Female −3.01 (11.69) 2.6e-01 −1.67
   (11.69) 1.0e + 00 −2.85 (11.30) 1.0e + 00 −2.46 (11.73) 1.0e + 00 −1.35
   (12.07) 1.0e + 00
   Diastolic blood pressure, mm Hg Female −1.25 (6.52) 9.5e-01 −1.19
   (5.75) 1.0e + 00 −1.66 (5.64) 6.1e-01 −2.29 (6.27) 2.6e-01 −1.43 (6.54)
   1.0e + 00
   Fasting insulin, mU/L Female −0.36 (3.25) 1.0e + 00 −0.30 (2.35)
   1.0e + 00 −0.02 (3.01) 1.0e + 00 −0.33 (3.63) 1.0e + 00 −0.90 (2.84)
   7.3e-01
   Whole-body fat, % Female −1.57 (1.70) 9.8e-09* −1.33 (2.22) 1.1e-02*
   −1.64 (1.85) 5.2e-05* −1.58 (1.98) 7.2e-05* −1.98 (2.07) 2.5e-04*
   Trunk fat, % Female −2.22 (2.15) 3.1e-10* −1.76 (2.78) 7.1e-03* −2.32
   (2.36) 1.2e-05* −2.31 (2.48) 2.6e-05* −2.60 (2.57) 1.1e-04*
   Fasting HDL cholesterol, mmol/L Female −0.04 (0.15) 2.7e-01 −0.03
   (0.14) 1.0e + 00 −0.05 (0.16) 4.7e-01 −0.05 (0.15) 5.9e-01 0.00 (0.14)
   1.0e + 00
   Fasting HDL cholesterol size, nm Female 0.03 (0.08) 1.7e-02* 0.05
   (0.09) 1.7e-01 0.02 (0.08) 6.0e-01 0.04 (0.08) 2.6e-02* 0.04 (0.09)
   9.1e-02
   FRS Female −0.65 (1.60) 5.5e-03* −0.46 (1.59) 1.0e + 00 −0.42 (1.47)
   6.8e-01 −0.62 (1.76) 3.1e-01 −0.68 (1.79) 3.4e-01
   [105]Open in a new tab

   SD standard deviation, SAT subcutaneous adipose tissue, BMI body mass
   index, FRS Framingham Risk Score.

   Significance levels were calculated using a two-sided linear mixed
   model. P values were adjusted for multiple tests using the Bonferroni
   correction method. An adjusted P value below 0.05 was considered
   significant. Significant effects are indicated by an asterisk.

Strong sex-dependent transcriptional response in subcutaneous adipose tissue
and muscle

   We investigated the effect of the GOTO intervention by comparing the
   transcriptome levels post intervention to the baseline transcriptome
   levels matched for each participant. We studied the transcriptional
   differences in blood, SAT, and muscle (Fig. [106]1, differential gene
   expression analysis, see “Methods”). In males, the number of
   significantly differentially expressed genes (DEGs) across the
   intervention varied considerably between blood (1), SAT (89), and
   muscle (251) (Fig. [107]2, Table [108]3, and Supplementary
   Datasets [109]1–[110]3). The ratio between up and downregulated DEGs
   also differed between the affected tissues: a slightly larger number of
   downregulated genes in SAT whereas over 80% of the DEGs were
   upregulated in muscle. Noteworthy, the muscle DEGs contained 14
   collagen genes, all of which were upregulated (Supplementary
   Dataset [111]3).

Fig. 1. Outline of the transcriptomic analysis of the Growing Old TOgether
trial.

   [112]Fig. 1
   [113]Open in a new tab

   A The participants of the Growing Old TOgether (GOTO) trial changed
   their lifestyle for a duration of 13 weeks. Both before and after the
   intervention measurements were performed, including anthropometrics,
   Dual-energy X-ray absorptiometry and blood metabolite levels. In
   addition, following a standardized nutritional challenge, postprandial
   blood, subcutaneous adipose tissue (SAT) and muscle tissue were
   sampled. Blood, SAT and muscle RNA-seq levels were studied and a
   differential analysis was performed to study the effect of the
   intervention on these three tissues. Subsequently, a pathway
   overrepresentation analysis was performed. B To study which tissue best
   represented the metabolic health changes, the association between the
   RNA-seq levels of the overrepresented pathways of the three tissues and
   metabolic health marker levels was studied. C Joint and Individual
   Variation Explained (JIVE) was applied to investigate whether there was
   a joint variation between the expression levels of the three tissues.
   JIVE decomposed the expression levels of each tissue (X) into the joint
   (J), individual (A), and the residual (R) effect, with the joint effect
   representing the shared variation across the expression levels of the
   three tissues. Subsequently, the drivers of the joint effect in blood
   were identified. Lastly, the association between the joint effect
   drivers in blood and the metabolic health markers was studied and
   compared to the postprandial blood individual effect expression levels
   (A).

Fig. 2. Intervention effect on postprandial blood, SAT and muscle gene
expression levels in men and women.

   [114]Fig. 2
   [115]Open in a new tab

   The effect of the intervention was studied using two-sided linear mixed
   model analysis. The significance levels were adjusted for multiple
   testing using the false discovery rate (FDR). The log2 fold change is
   plotted on the x axis, the -log10 FDR-adjusted P value is plotted on
   the y axis. The dashed horizontal line represents the significance
   threshold of FDR 0.01, the two vertical lines represent the log2 fold
   change thresholds of −0.25 and 0.25. Genes with an absolute log2 fold
   change ≥0.25 and an FDR-adjusted P value < 0.01 were considered
   significantly differentially expressed. Each dot represents a gene.
   Yellow dots represent the significantly differentially expressed genes
   (DEGs). Gray dots represent genes with an absolute log2 fold change <
   0.25 and/or FDR ≥ 0.01. Significantly differentially expressed genes
   with the strongest log2 fold changes were labeled with their gene
   symbol. SAT subcutaneous adipose tissue.

Table 3.

   The 13-week lifestyle intervention had the strongest effect on
   postprandial SAT and muscle transcriptomics
   Sex Tissue State Direction of effect Expressed genes Genes with
   Log2FC ≥ 0.25 Genes with
   FDR < 0.01 Genes with
   Log2FC ≥ 0.25 &
   FDR < 0.01
   Male Blood Postprandial Downregulated 8602 52 1 1
   Male Blood Postprandial Upregulated 10,093 45 1 0
   Male SAT Postprandial Downregulated 10,099 402 123 48
   Male SAT Postprandial Upregulated 9614 173 180 41
   Male Muscle Postprandial Downregulated 9413 228 139 46
   Male Muscle Postprandial Upregulated 7166 674 239 205
   Female Blood Postprandial Downregulated 9619 63 0 0
   Female Blood Postprandial Upregulated 9143 32 0 0
   Female SAT Postprandial Downregulated 9860 254 331 92
   Female SAT Postprandial Upregulated 9599 143 267 76
   Female Muscle Postprandial Downregulated 9464 292 127 46
   Female Muscle Postprandial Upregulated 7570 827 132 122
   [116]Open in a new tab

   SAT subcutaneous adipose tissue.

   For females, the number of DEGs across the different tissues were
   somewhat similar as in males, but the effect sizes were smaller
   (Fig. [117]2 and Table [118]3): zero DEGs in blood, 168 in SAT, and 168
   in muscle (Supplementary Datasets [119]4 and [120]5). Like the male
   samples, more than half of the DEGs in SAT were downregulated, while in
   muscle over two-thirds of significant DEGs were upregulated. The female
   muscle DEGs contained 7 collagen genes, all of them upregulated
   (Supplementary Dataset [121]5).

   Overall, in both males and females, the intervention had hardly any
   effect on the blood transcriptome, while in SAT both up- and
   downregulated genes were observed, whereas in muscle the majority of
   the DEGs were upregulated.

Lipid and collagen-related pathways enriched in SAT and muscle response,
respectively

   We then investigated which pathways were associated with the found DEGs
   (pathway overrepresentation analysis, see Methods section). In male
   SAT, five pathways were significantly overrepresented (Fig. [122]3 and
   Supplementary Dataset [123]6). The three upregulated pathways were
   involved with HDL remodeling, nuclear receptor signaling and
   cholesterol transport, while the downregulated pathway was involved in
   fertilization, the remaining pathway (equally down -and upregulated)
   was involved in ligand clearance. In male muscle, all 18 enriched
   pathways were upregulated. The majority of these were involved in
   collagen formation or extracellular matrix formation/degradation
   (Fig. [124]3 and Supplementary Dataset [125]6). The remaining pathways
   were involved in myogenesis (NCAM1 pathways), mesenchymal stem cell
   differentiation (PDGF pathway), scavenger receptors (binding and
   uptake, class A receptors), and protein O-glycosylation.

Fig. 3. Significantly overrepresented pathways in postprandial SAT and muscle
transcriptome.

   [126]Fig. 3
   [127]Open in a new tab

   A one-sided functional enrichment test was performed to identify the
   overrepresented pathways. The overrepresented pathways are plotted on
   the y axis. Overrepresented pathways in the male samples are plotted on
   the left, overrepresented samples in female samples, on the right. The
   fill color represents the normalized direction of effect: dark blue
   ( − 1) all significantly differentially expressed genes in the pathway
   were underexpressed, dark red (1) all significantly differentially
   expressed genes in the pathway were overexpressed. P values were
   adjusted for multiple testing using the Bonferroni correction method.
   Significance is indicated by the asterisks (*P adjust <0.05, **P adjust
   <0.01, ***P adjust <0.001). SAT subcutaneous adipose tissue.

   In female SAT, seven pathways were significantly overrepresented
   (Fig. [128]3 and Supplementary Dataset [129]6). The three upregulated
   pathways were involved in signaling pathways and HDL remodeling. The
   four downregulated pathways were related to MAPK signaling and
   CO[2]/O[2] exchange. However, the three CO[2]/O[2] pathways were only
   overrepresented due to the same three genes (Supplementary
   Dataset [130]6). In female muscle, all eight overrepresented pathways
   were upregulated (Fig. [131]3 and Supplementary Dataset [132]6), six of
   which were involved in collagen formation and extracellular matrix
   organization/degradation. The remaining pathways were involved in in
   netrin-1 signaling and osteoblast differentiation.

   Taken together, both in males and females, the overrepresented pathways
   in muscle were largely involved in collagen and extracellular matrix
   remodeling. In SAT, there were fewer enriched pathways which were less
   consistent in terms of direction (both -up and downregulated). The
   overrepresented pathways in SAT were involved in signaling, O[2]/CO[2]
   exchange, HDL remodeling, and signaling.

Response of tissue-specific overrepresented pathways reproduced across SAT
and muscle, not in blood

   Next, we questioned whether there was a transcription response common
   to the three tissues. Hereto, we examined the response of the mean
   expression of the DEGs within each overrepresented pathway of SAT and
   muscle (Fig. [133]3). For example, the mean expression of the
   male-upregulated SAT DEGs involved in the HDL remodeling pathway was
   also significantly positively upregulated in muscle, whereas it was
   non-significantly upregulated in blood (second row in top panel of
   Fig. [134]4). Similarly, in females, we noted correspondence between
   SAT and muscle, albeit weaker than in males. The HDL remodeling pathway
   was weakly upregulated in both SAT and muscle but had no response in
   blood (second row, third panel from the top, Fig. [135]4). Since there
   was only one significant DEG in postprandial blood (Fig. [136]2 and
   Table [137]3), no overrepresented blood down- or upregulated pathways
   were present.

Fig. 4. Significantly overrepresented pathway expression levels in SAT and
muscle are significantly associated with the intervention time point and
metabolic health markers.

   [138]Fig. 4
   [139]Open in a new tab

   Associations between the overrepresented pathways and metabolic health
   markers were calculated using a two-sided linear mixed model.
   Overrepresented pathways are plotted on the y axis. Intervention time
   point and metabolic health markers are plotted on the x axis. Colors
   indicate the estimated effect between the mean expression in the
   pathways and the intervention effect/metabolic health markers: blue, a
   negative effect, white, no effect, red, a positive effect. P values
   were corrected for multiple testing using the false discovery rate. The
   significance level is indicated by the asterisks: *FDR <0.05, **FDR
   <0.01, ***<0.001.

   In general, we noted a similar behavior of the overrepresented
   postprandial SAT pathways across the three tissues, both in males and
   females (Fig. [140]4). In addition, two overrepresented SAT pathways
   (HDL remodeling, NR1H3 & NR1H2 gene regulation), were significantly
   positively associated with the intervention time point in postprandial
   muscle expression of males. In contrast to the overrepresented SAT
   pathways, the intervention had an opposite effect on the
   overrepresented muscle pathways in the SAT transcriptome, when compared
   to the muscle expression levels. In addition, the ECM proteoglycans
   pathway, which was significantly upregulated in muscle, had a
   significant negative effect in SAT (in males only). The intervention
   had a weak effect on the remaining overrepresented muscle pathways, in
   either sex. Lastly, the intervention had hardly an effect on the
   postprandial blood expression levels of any of the overrepresented
   pathways, showing that the genes that were differentially expressed in
   SAT and/or muscle were not changed by the intervention in blood.

   In summary, when investigating the intervention effect of the genes
   strongest affected by the intervention, no overlap of transcriptional
   response was observed between blood and SAT or muscle. Some
   relationships did exist within the transcriptional response between SAT
   and muscle, although not always consistent in the direction of effect.

At baseline, the transcriptomes of postprandial blood, SAT, and muscle are
weakly associated with the metabolic health status

   To investigate the interplay between the transcriptome and metabolic
   health at baseline, we calculated the association between the baseline
   mean expression of the overrepresented pathways to the baseline
   measurements of ten clinical metabolic health markers: I) two DEXA scan
   body composition measurements: whole-body fat%, trunk fat%; II) seven
   traditional metabolic health markers: BMI, Waist Circumference (WC)
   fasting insulin, fasting HDL cholesterol, fasting HDL cholesterol size,
   SBP, DBP; and III) the Framingham Risk Score (FRS), which is a
   cardiovascular risk score (Supplementary Fig. [141]2).

   To account for the differences in the baseline metabolic health marker
   levels between the different subsets (Table [142]1), we performed this
   analysis only on the participants with transcriptome measurements in
   all three tissues (57; 33 males, 24 females) (Supplementary
   Fig. [143]1).

   At baseline, none of the associations between the expression levels of
   the different pathways in the three tissues and health marker levels
   were significant. However, some trends were present. In males, the SAT
   transcriptome was strongest associated to the metabolic health marker
   levels. In SAT, three of the overrepresented pathways (nuclear receptor
   signaling, HDL remodeling, cholesterol transport) had a negative
   association with whole-body and trunk fat%, BMI, WC, fasting insulin,
   the FRS and a positive association with the HDL cholesterol level and
   size. Interestingly, the pathway scavenger receptor ligand binding and
   all the pathways overrepresented in muscle, showed an opposite trend
   with metabolic health, compared to the three aforementioned SAT
   pathways. The blood transcriptome was weakly associated to the
   metabolic health status. However, the association between the blood
   expression levels and metabolic health, was opposite to the SAT
   transcriptome associations reflecting that changes in blood cells
   poorly relate to those in the tissues. Lastly, the expression levels of
   muscle showed a weak association to the metabolic health levels, except
   for a positive association between signaling by the nuclear receptor
   pathway, the HDL measurements and DBP.

   In females, the overrepresented SAT pathways were strongest positively
   associated with metabolic health markers when expressed in postprandial
   blood. The effect sizes were largest between blood expression of the
   signaling pathways, O2/CO2 exchange pathways, and whole-body fat%,
   trunk fat%, DBP and SBP. In postprandial SAT, only the pathway MAPK
   family signaling cascades showed a positive association with whole-body
   fat%, trunk fat%, BMI, and WC. In postprandial muscle, the
   overrepresented muscle pathways showed a weak negative association with
   the BMI, WC, fasting insulin and the body composition measurements.

   Taken together, the baseline expression levels in none of the three
   tissues were significantly associated to the metabolic health status.
   However, when comparing the three tissues, the postprandial SAT
   transcriptome best captured the baseline metabolic health status in
   males, while in females the postprandial blood transcriptome was
   strongest associated with the baseline metabolic health status.

Especially SAT- and muscle-specific overrepresented pathways associate with
health markers across the intervention

   To better understand the differential expressed transcriptome, we
   investigated the associations between the mean expression of the
   overrepresented pathways and the metabolic health markers across the
   two time points of the intervention (“Methods”, Fig. [144]4). Since the
   intervention effect on some of the health markers differed between the
   subgroups (Table [145]2), which can cause differences in effects due to
   differences in subsets, the relation between the expression data and
   metabolic health markers was performed on the overlapping blood, SAT
   and muscle samples (57; 33 males, 24 females) (Supplementary
   Fig. [146]1).

   In general, the postprandial blood expression levels were weakly
   associated to the metabolic health markers (Fig. [147]4). However, in
   males, the postprandial blood expression levels of two pathways (HDL
   remodeling, cholesterol transport) were significantly negatively
   associated to trunk fat%, while two other pathways (ligand binding &
   collagen fibril crosslinking) were significantly positively associated
   to trunk fat% and the fasting HDL cholesterol size. In addition, in
   females, the pathways involved in O[2]/CO[2] exchange were
   significantly positively associated to the changes in DBP, while DBP
   and netrin-1 signaling were significantly negatively associated.

   The postprandial SAT expression levels of the majority of both SAT and
   muscle-overrepresented pathways were significantly associated to the
   change in DEXA body composition parameters (whole-body fat%, trunk
   fat%), three of the seven traditional metabolic health markers (BMI, WC
   and fasting HDL cholesterol level) and the FRS, in both sexes, and with
   the fasting HDL cholesterol size, DBP and SBP only in men. The SAT
   pathways involved in nuclear receptor signaling, HDL remodeling
   cholesterol transport and signaling transduction were significantly
   negatively associated to whole-body fat%, trunk fat, BMI and WC, and
   positively associated to the HDL cholesterol measurements. Conversely,
   the remaining SAT pathways (fertilization, ligand binding and uptake,
   MAPK cascades) showed an opposite trend with similar strength of
   effect. The expression levels of the muscle-overrepresented pathways
   were positively associated to all metabolic health markers, excepting
   the fasting HDL level and size. Interestingly, only the overrepresented
   pathways of the muscle tissue were significantly positively associated
   to the two blood pressure measurements.

   The transcriptome of postprandial muscle was weakly associated to the
   changes in the metabolic health markers. In males, pathways involved in
   HDL remodeling, NR1H3 & NR1H2 gene regulation, and TSR O-glycosylation
   and PpS, were significantly negatively associated to BMI and WC,
   respectively. In females, there was one significant negative
   association between the pathway nuclear receptor signaling and fasting
   HDL cholesterol size.

   Interestingly, in males, the expression levels of HDL remodeling and
   NR1H3/NR1H2 gene regulation were significantly associated to
   weight-related metabolic health markers in all three tissues. Moreover,
   the direction of effect between the expression of these pathways and
   trunk fat%, whole-body fat%, WC, and BMI was negative in all three
   tissues.

   Compared to the baseline associations between the pathway expression
   and metabolic health marker levels (Supplementary Fig. [148]2), the
   effects were stronger across the intervention for both the SAT and
   muscle transcriptomes. Contrarily, in postprandial blood, the
   expression of the overrepresented muscle pathways was strongly
   associated at baseline than across the intervention. The direction of
   effect remained the same for the overrepresented SAT pathways between
   the two analyses, while the associations between the
   muscle-overrepresented pathways and part of the health markers
   (whole-body fat%, trunk fat%, BMI, WC) were opposite at baseline and
   across the intervention, when expressed in blood and muscle.

   Taken together, especially in the SAT transcriptome, the SAT and
   muscle-overrepresented pathways reflected changes in DEXA body
   composition markers (whole-body and trunk fat%), classical metabolic
   health markers (BMI, WC, fasting HDL cholesterol level and size, DBP,
   SBP) and the FRS. Many effects were similar in males and females,
   although more pronounced in males.

Integrated analysis reveals male postprandial blood, SAT, and muscle
transcriptome share a joint variation

   To explore the tissue dependency in a more intricate way, we captured
   the joint transcriptional reaction to the intervention across the three
   different tissues using Joint and Individual Variation Explained (JIVE)
   analysis (“Methods”, Supplementary Fig. [149]3). The joint
   representation is a linear subspace spanned by the genes, analogous to
   a principal component analysis (PCA), but that now also explains the
   joint variation in expression in all three tissues across the
   intervention (“Methods”). Hence, although informed by all three
   tissues, the blood expression of a participant (before and after the
   intervention) can be mapped to this joint subspace without needing
   information about the muscle or SAT transcriptome. In other words, the
   blood transcriptome of a participant is decomposed in three
   (independent) transcriptome terms: (1) a term that reflects the joint
   transcriptomic variation in blood, SAT, and muscle, (2) a term
   reflecting the remaining structured variation in blood only (also
   referred to as the individual variation), and (3) a term that
   represents the residual noise in the blood transcriptome, i.e., the
   signal that is not explained by the joint and individual terms.
   Subsequently, we can measure the differential expression across the
   intervention in either the joint subspace (of blood, SAT, and muscle)
   or the individual subspaces (for each tissue type). The number of joint
   and individual components are determined through permutation testing
   (“Methods”).

   In the male samples, JIVE identified 2 joint components for the shared
   transcriptomic variation in blood, SAT and muscle, and 11, 15, and 15
   individual components for the blood, SAT, and muscle, respectively
   (Supplementary Dataset [150]7). In postprandial blood and SAT, the
   joint components together explained 8% and 7.2% of the total variation
   in those tissues, respectively. In the postprandial muscle
   transcriptome, both joint components explained 31.3% of the variation.
   In female samples, JIVE did not identify a joint term that was
   independent of the individual terms (Supplementary Fig. [151]4).

In male samples, the drivers of the joint components were involved in
phospholipid metabolism and the immune system

   To investigate what genes expressed in postprandial blood were the
   drivers of the joint effect, the 250 genes that had the largest loading
   factors for each of the two joint components were selected,
   respectively (“Methods”). Both sets of genes were subsequently used in
   a pathway enrichment analysis. In postprandial blood, the genes driving
   the first joint component (JC1) that explains most of the joint
   variance across the tissues were involved in phospholipid metabolism,
   while the genes driving the second joint component (JC2) were involved
   in interferon signaling, inflammation, and other aspects of the immune
   system (Fig. [152]5).

Fig. 5. Significantly overrepresented pathways in the top 250 drivers of the
postprandial blood joint effect.

   [153]Fig. 5
   [154]Open in a new tab

   A one-sided statistical test was performed to identify the
   overrepresented pathways. P values were adjusted for multiple testing
   using the Bonferroni correction method. The overrepresented pathways
   are plotted on the y axis. Y axis is ordered by significance, the
   strongest significantly overrepresenting pathways first. The x axis
   represents -log10 of the Bonferroni adjusted P value.

Phospholipid metabolism genes that drive the joint effect also capture the
intervention effect individually

   Next, we wondered whether the genes driving the significant effects of
   JC1 and JC2 also showed an intervention effect by themselves. Within
   both sets of 250 genes that had the highest loading factors for each of
   the joint components, we selected those genes that overlapped with the
   found enriched pathways. Interestingly, we found a remarkable
   difference between the intervention effect of the genes involved in
   phospholipid metabolism and those involved in immunity (Fig. [155]6):
   the joint effect of the genes involved in phospholipid metabolism was
   more strongly influenced by the intervention than the individual
   effect, however, neither effect crossed the significant threshold after
   adjustment for multiple testing (Fig. [156]6). Compared to the
   phospholipid metabolism genes, the effect sizes in both the joint nor
   individual effect of genes involved in the immune system were
   considerably weaker. These effects were also non-significantly
   associated with the intervention effect.

Fig. 6. In postprandial blood, the joint component 1 drivers strongly
captured the intervention effect while joint component 2 drivers were
significantly associated to whole-body fat% and trunk fat% levels.

   [157]Fig. 6
   [158]Open in a new tab

   Associations between the joint component drivers and the intervention
   effect & metabolic health markers were calculated using a two-sided
   linear mixed model. The genes were plotted on the y axis, the
   intervention effect and metabolic health markers were plotted on the x
   axis. Colors indicate the direction of the effect: blue, a negative
   effect, white; no effect, red; a positive effect, the darker the color,
   the stronger the effect. The P values were adjusted using the false
   discovery rate. Significance is indicated by the asterisks (*FDR
   <0.05).

Immune genes that relate to the joint effect capture metabolic health effects
of the intervention

   Next, we investigated whether changes in the genes involved in
   phospholipid metabolism and immunity associated with changes in the
   metabolic health markers. Here we found that neither the joint nor
   individual effect of the phospholipid genes associated to these health
   marker effects (Fig. [159]6). In contrast, the joint effect of the
   genes involved in the immune system did strongly associate with markers
   of body composition (whole-body and trunk fat%) while, except for RAB44
   and RAPGEF3 (which was associated to the fasting HDL cholesterol
   level), the individual effect did not. Hence, it is possible to detect
   the effects of the intervention in the postprandial blood
   transcriptome, but only when the individual blood effect has been
   removed.

Variance in the muscle and SAT transcriptome explain the selection of
contributing genes to the joint components of blood differently

   To investigate which of the two tissues, SAT or muscle, correlated most
   with the joint components in blood, we calculated the correlations of
   the top 250 genes with the highest loading factors for each of the
   joint components (JC1 and JC2) in blood with those in muscle and SAT
   (Supplementary Fig. [160]5). The JC1 blood genes showed increasingly
   higher absolute correlations with the JC1 and JC2 muscle genes, whereas
   the JC2 blood genes showed an opposite behavior and were merely not
   correlated with the JC1 or JC2 muscle genes. On the other hand, the JC1
   and JC2 SAT genes showed a stronger absolute correlation with the JC2
   blood genes as opposed to the JC1 blood genes. These results indicate
   that the JC1 blood genes (dominated by genes involved in phospholipid
   metabolism) are largely correlated with the variance in the muscle
   transcriptome, the JC2 blood genes (dominated by genes involved in the
   immune system) are more correlated with the variances in the SAT
   transcriptome.

Discussion

   We investigated the effects in men and women of a mild combined
   lifestyle intervention (Growing Old TOgether trial, trial registration
   number GOT NL3301 ([161]https://onderzoekmetmensen.nl/nl/trial/27183),
   [162]NL-OMON27183 in older adults on the postprandial transcriptome of
   blood, subcutaneous adipose tissue (SAT) and muscle tissue, and related
   the response to changes in parameters of metabolic health. Furthermore,
   using JIVE, we explored the shared variation between the three
   transcriptomes. In both sexes, the 13-week combined lifestyle
   intervention had virtually no effect on the postprandial blood
   transcriptome, while the postprandial SAT and muscle transcriptomes
   responded significantly. In postprandial SAT, pathways involved in HDL
   remodeling, cell signaling, and CO[2]/O[2] exchange (both sexes),
   ligand binding by scavenger receptors, NR1H3/NR1H2 cholesterol-related
   gene expression and fertilization (male only), and signal transduction
   and MAPK signaling cascades (female only) were overrepresented, while
   in the postprandial muscle, pathways involved in collagen,
   extracellular matrix remodeling, protein O-glycolysation (male only)
   and osteoblast differentiation (female only) were significantly
   overexpressed.

   The response of the SAT transcriptome in both sexes associated most
   strongly with changes in the Framing Risk Score (FRS), and metabolic
   health, including HDL cholesterol level and size, fasting insulin, and
   specifically in body composition markers (whole-body and trunk fat%,
   BMI and Waist Circumference (WC)), and so did a part of the changes in
   the muscle transcriptome (whole-body and trunk fat%, BMI, fasting HDL
   cholesterol, WC and the FRS). In postprandial blood, the expression of
   a few pathways was associated with trunk fat%, fasting HDL cholesterol
   size, and diastolic blood pressure (DBP). Interestingly, in males, the
   expression levels in all three tissues of pathways involved in HDL
   remodeling and NR1H3 & NR1H2 regulated gene expression were
   significantly negatively associated to one or more body composition
   makers, which might indicate a shared biological process between the
   three tissues. We did observe sex differences in the association effect
   sizes. Lastly, we identified shared variation between the three
   transcriptomes, but only in the tissues of men. We identified in the
   blood transcriptome a joint intervention effect dominated by genes
   involved in phospholipid metabolism, largely influenced by genes
   overlapping with changes in the muscle transcriptome, and a second
   joint intervention effect dominated by genes involved in the immune
   system, overlapping with changes in the SAT transcriptome. The latter
   associated with changes in body composition (whole-body and trunk fat%)
   with larger effect sizes and higher significance levels than in the
   original transcriptome of postprandial blood.

   The effect of lifestyle interventions on the human transcriptome has
   mainly been reported for dietary and physical activity regimes
   separately, typically in metabolically unhealthy or frail
   participants^[163]21,[164]23–[165]25,[166]30. This makes a direct
   comparison with the response of the healthy, non-diabetic, older
   adults^[167]1 to the GOTO combined lifestyle intervention difficult.
   Nevertheless, we do find similarities to dietary interventions. Like in
   GOTO, nutritional interventions with a mild energy restriction (20-25%,
   for 4–12 weeks) hardly affected the blood transcriptome^[168]23. In
   adipose tissue, caloric restriction interventions changed the
   expression of genes involved in glucose and lipid metabolism, signal
   transduction, stem cell maintenance and
   vascularization^[169]18,[170]20,[171]24,[172]25,[173]30, the former
   three of which were also overrepresented in the SAT transcriptome of
   the GOTO trial (Fig. [174]3). Lastly, in overweight participants or
   participants with obesity of a weight-loss intervention study (6
   months, diet, physical activity separate and combined), genes involved
   in signaling pathways, translation, cell life span and ATP binding were
   significantly overrepresented^[175]31, part of which we also identified
   in GOTO.

   The effects of the muscle transcriptome to physical activity changes
   were reported in both anabolic and catabolic interventions, the latter
   being more similar to the regimen in GOTO. In line with the results
   found in GOTO, a systematic review investigating the effects of 21
   activity interventions on the ECM in muscle, found that both types of
   activity interventions had a positive effect on collagen level and
   identified ECM adaptations related to muscle remodeling in both young
   and older participants^[176]26. Not all gene expression effects are
   common to all interventions. A 6-months aerobic, resistance, or aerobic
   + resistance training regimen in older adults with obesity changed the
   expression of genes involved in myogenesis, autophagy, mitochondrial
   biogenesis and inflammation, in all three training regimens^[177]22,
   which was not observed for the muscle transcriptome in the GOTO trial.
   This difference may be ascribed to the better health at baseline of
   GOTO individuals and/or the duration and intensity of this in essence
   anabolic intervention. Another anabolic 6-month resistance training
   intervention in frail and healthy older adults, showed effects similar
   to GOTO; an increase in expression levels of collagen and extracellular
   matrix-related genes^[178]21. Finally, in mice a combined caloric
   restriction and physical activity intervention reported DEGs involved
   in MAPK signaling, metabolic processes, cholesterol metabolism,
   extracellular matrix organization^[179]36, which we also found in the
   GOTO trial (Fig. [180]3).

   There is a number of other differences in study designs as compared to
   reported interventions. GOTO was a personalized intervention, to mimic
   a realistic situation when people change their lifestyle. Consequently,
   there was a considerable variation in the instructed lifestyle change
   between the participants; some changed their diet more, while other
   focused more in a change in physical activity. Furthermore, the
   transcriptome in GOTO was measured in tissues taken after a
   standardized meal^[181]17, which is usually not done.

   A direct association between the expression levels in adipose tissue
   and muscle, and classical metabolic health markers is generally not
   looked into, making it difficult to relate the effects identified in
   GOTO (Fig. [182]4). However, one cohort study of BMI discordant
   monozygotic twins (ΔBMI ≥ 2.5 kg/m^2) reported on adipose tissue and
   skeletal muscle transcriptome differences^[183]42. This study
   identified DEGs involved in ECM remodeling, DNA/RNA metabolism,
   mitochondrial metabolism (both adipose tissue and skeletal muscle),
   fatty acid metabolism, MAPK and AMPK signaling (adipose tissue only),
   and amino acid metabolism (skeletal muscle only)^[184]42, which largely
   overlap with the overrepresented pathways in GOTO (Fig. [185]3). In
   addition, it was emphasized that combined differential gene expression
   scores of the adipose tissue transcriptome were stronger associated
   with classical metabolic health markers (including BMI, whole-body
   fat%, HDL cholesterol) than those of muscle transcriptome^[186]42,
   which we also observed in GOTO (Fig. [187]4).

   When investigating the intervention effect of the overrepresented
   pathways of one tissue in the other two tissues, we identified that the
   intervention had an effect on 2 out of 5 SAT overrepresented pathways
   in the muscle transcriptome (Fig. [188]4). However, the other way
   around, when looking into overrepresented muscle pathways expressed in
   SAT, the intervention only had a significant effect on only 1 out 18
   pathways. This indicated that the muscle transcriptome captured some of
   the effects on the SAT transcriptome, possibly by a SAT component in
   the muscle tissue. In addition, in males, the expression of two
   pathways (HDL cholesterol, NR1H3 & NR1H2 regulate gene expression
   linked to cholesterol transport and efflux) and one or more of the body
   composition measurements (whole-body fat%, trunk fat%, WC, BMI) was
   significantly negatively associated in all three tissues, which could
   indicate a shared component between the three tissues as well
   (Fig. [189]4).

   Using the JIVE data integration approach^[190]40, we have identified a
   shared variation between the three tissues in the male samples
   represented by two joint components (Supplementary Figs. 3 and 4 and
   Supplementary Dataset [191]7). The genes that drove these two joint
   components were involved in phospholipid metabolism and the immune
   system, respectively (Fig. [192]5). Like the effects in the
   postprandial muscle transcriptome on the pathway expression levels
   (Fig. [193]4), the genes driving the first joint component (JC1) in
   blood showed a strong intervention effect but did not capture metabolic
   health effects (Fig. [194]6). Moreover, these genes were also more
   correlated to variances in the muscle transcriptome than the SAT
   transcriptome (Supplementary Fig. [195]5). Conversely, similarly to the
   effects in postprandial SAT, the genes driving the second joint
   component (JC2) in blood strongly captured the metabolic health effects
   of the intervention but had a weaker association to the intervention
   itself. This was also confirmed by a higher correlation with variances
   in the SAT transcriptome than the muscle transcriptome of the genes
   belonging to this second joint blood component (Supplementary
   Fig. [196]5). From these results, we postulated that JC1 in blood was
   largely driven by the muscle transcriptome, while JC2 in blood was
   primarily driven by the SAT transcriptome.

   This trial faced several limitations, which need to be considered when
   interpreting the results. First, the number of participants with
   transcriptomics measurements of postprandial blood (88; 45 males, 24
   females), postprandial SAT (78; 38 males, 40 females), and postprandial
   muscle (82; 48 males, 34 females), was relatively low, compared to the
   complete number of participants compliant to the GOTO trial (153; 75
   males, 78 females). The variation in the instructed lifestyle changes
   in combination with the low number of samples and slight differences at
   baseline (Table [197]1), could also have led to a large variation in
   outcomes when comparing participants with postprandial blood, SAT, and
   muscle transcriptome measurements (Table [198]2). To circumvent part of
   this variation, the effects between the transcriptome and metabolic
   health markers were only studied within the participants with
   transcriptomic measurements in postprandial blood, SAT, and muscle (57;
   33 males, 24 females) (Supplementary Fig. [199]1). Second, the group of
   participants with transcriptomic measurements of all three tissues was
   also used for the JIVE analysis. In females, the JIVE model did not
   identify a shared variation, this could be because of the shared
   effects between the tissues were too weak, which was likely attenuated
   by the low number of participants (24). In males, the JIVE analysis did
   identify two joint components between the transcriptomes of the three
   tissues. Lastly, after the intervention period, no further follow-up
   was performed on the participants of the GOTO trial, thus we cannot say
   whether the transcriptomic changes as result of the intervention have
   any long-term effects.

   Overall, we were able to identify considerable effects of a 13-week
   moderate nutritional and physical activity intervention on the
   transcriptome of SAT and muscle of healthy older adults. We
   demonstrated that the changes in the expression of genes in metabolism
   pathways in SAT, and collagen and muscle remodeling pathways in muscle
   associated with beneficial features of overall metabolic health. These
   health effects were best captured by the postprandial SAT
   transcriptome. We identified a joint space of three tissues in
   postprandial blood, which was able to capture some of the effects in
   the other two tissues, even though postprandial blood alone did not
   capture this. We propose that this method could be used to estimate
   part of the effect of an intervention on SAT and muscle transcriptomes,
   when only blood expression data is available.

Methods

Trial design

   The Growing Old TOgether (GOTO) trial protocol was approved by the
   Medical Ethical Committee of the Leiden University Medical Center
   before the start of the trial (P11.187). In accordance with the
   Declaration of Helsinki, the GOTO trial obtained informed consent from
   all participants prior to their entering the trial. This trial was
   registered at the Dutch Trial Register
   ([200]https://onderzoekmetmensen.nl/en/trial/27183) as GOT NL3301 and
   can also be found at the international clinical trials registry
   platform as [201]NL-OMON27183. The first participant was enrolled on
   the 11th of June 2012, while the last participant was enrolled on the
   17th of January 2013.

   In the Leiden Longevity study, from which the GOTO participants were
   recruited, sex was first self-reported and subsequently verified with
   birth certificates and genotype data. Inclusion criteria were as
   follows: middle-aged (≤ 75 years) couples consisting of offspring from
   long-lived siblings and his/her current partner or in incidental case
   as single participant, BMI levels ≥23 and ≤35 kg/m^2. Exclusion
   criteria: type I or type II diabetes (on diabetic medication),
   individuals who have lost or gained ≥3 kg in the 6 months prior to
   screening, individuals engaged in heavy/intensive physical activity
   (top sport or physically heavy work), any disease or condition that
   seriously affects body weight and/or body composition including active
   types of cancer, heart failure (NYHA III/VI), COPD (GOLD III/VI),
   recent (3 months prior to intervention) immobilization for longer than
   1 week, psychiatric or behavioral problems (eg, history or clinical
   manifestation of any eating disorders, vegan dietary lifestyle, major
   depression), using one or more of the following medication: thyroid
   medication, immunosuppressive drugs (e.g., prednisone, methotrexat,
   biologicals (TNF-alpha antagonists etc), current participation in any
   other intervention study or weight management program, exclusion for
   biopsy: use of anticoagulantia (e.g., coumarines, carbaspirin calcium),
   presence of exclusion criteria for MR imaging (3 tesla and 7 tesla):
   claustrophobia, pacemakers and defibrillators, nerve stimulators,
   intracranial clips, intraorbital or intraocular metallic fragments,
   cochlear implants, ferromagnetic implants (e.g., thoracic implant for
   scoliosis), inability to lie supine during for 45 min, not having a
   general practitioner.

   Sex swaps were verified and corrected when necessary. Sex was
   considered in the trial design. Previous research has shown that the
   GOTO intervention had different effects on male and female
   participants^[202]1. Part of this was due to differences in their daily
   activity patterns^[203]41. Since the earlier results highlighted the
   sex-specific effects of the GOTO trial, subsequent analyses were
   performed in a sex-stratified manner.

   The GOTO trial was a 13-week nonrandomized, noncontrolled, 1 arm open
   masking design lifestyle intervention trial with 164 healthy older
   adult participants (83 males, 81 females, mean age 62.9 + − 5.7, mean
   BMI 26.9 + − 2.5). The participants underwent a 25% reduction in energy
   expenditure, divided over 12.5% decreased caloric intake and 12.5%
   increased physical activity, as described in ref. ^[204]1. Before the
   intervention, baseline energy intake and expenditure were assessed
   through a 150-item food frequency questionnaire^[205]43 and the
   International Physical Activity Questionnaire-Short Form
   (IPAQ-SF)^[206]44. Personalized intervention guidelines were prescribed
   by a dietician and a physiotherapist, in the consultation with the
   participant’s preferences and physical abilities. The dietary