Re: 고강도 유산소 운동 --＞ 장내미생물 변화 SCFA 수면개선!! 2023 nature

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고강도 유산소 운동

--> 장내 미생물 변화

--> 혈액내 SCFA 증가

--> 불면 개선...

Dysbiosis

--> 대변내 SCFA 증가(대장에서 단쇄지방산 흡수를 못함)

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Increased physical activity improves gut microbiota composition and reduces short-chain fatty acid concentrations in older adults with insomnia

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• Published: 10 February 2022

Increased physical activity improves gut microbiota composition and reduces short-chain fatty acid concentrations in older adults with insomnia

• Faiga Magzal, 
• Tamar Shochat, 
• Iris Haimov, 
• Snait Tamir, 
• Kfir Asraf, 
• Maya Tuchner-Arieli, 
• Carmel Even & 
• Maayan Agmon 

Scientific Reports volume 12, Article number: 2265 (2022) Cite this article

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Abstract

Physical activity (PA) can improve functional abilities, well-being, and independence in older adults with insomnia. Studies have shown that PA may be linked to changes in the gut microbiota composition and its metabolites’ concentrations. This association among older adults with insomnia, however, is yet to be determined. We explored the relationships between physical activity (PA) levels, gut microbiota composition, and short-chain fatty acid (SCFA) levels in this population. Forty-nine community-dwelling adults with insomnia symptoms, aged 65 and older, participated in this study. Their average daily step-count and sleep continuity measures over a two-week period were calculated based on Actigraphic recordings. Each participant provided fecal samples for the microbiome and SCFA analyses, anthropometric measures, and information via questionnaires on medical history and food consumption. The gut microbiota composition and SCFA concentrations were determined by next-generation sequencing and Gas chromatography-mass spectrometry, respectively. Participants were divided into two groups (more and less active) according to their median step/day count. We compared the microbiota abundance and SCFA concentrations between groups and performed correlation analysis between gut microbiota abundances and study variables. Different microbiota taxa in each PA group and increased SCFAs in feces of less active individuals were found. Changes in step counts were positively or negatively associated with the relative abundance of 19 ASVs, 3 microorganisms at the family level, and 11 microorganisms at the genus level. Furthermore, significant associations were discovered among physical activity, gut microbiota, SCFAs, and sleep parameters. Our findings provide new insights on the relationship between PA, gut microbiota composition, and primary metabolites in older adults with insomnia.

신체 활동(PA)은

불면증을 겪는 노인들의 기능적 능력, 복지, 독립성을 개선할 수 있습니다.

연구들은

PA가 장내 미생물군집 구성과 그 대사산물 농도의 변화와 연관될 수 있음을 보여주었습니다.

그러나

불면증을 겪는 노인들에서의 이 연관성은

아직 명확히 규명되지 않았습니다.

본 연구에서는 이 인구 집단에서

신체 활동(PA) 수준,

장내 미생물군 구성,

단쇄 지방산(SCFA) 수준 간의 관계를 탐구했습니다.

불면증 증상을 보이는 65세 이상 지역 거주 성인 49명이 본 연구에 참여했습니다.

2주 동안의 평균 일일 걸음 수와 수면 연속성 지표는

Actigraphic 기록을 기반으로 계산되었습니다.

각 참가자는 미생물군집 및 SCFA 분석을 위한 분변 샘플, 인체 측정 데이터, 의료 이력 및 식습관 관련 설문지 정보를 제공했습니다. 장내 미생물군집 구성은 차세대 시퀀싱을 통해, SCFA 농도는 가스 크로마토그래피-질량 분석법을 통해 각각 측정되었습니다. 참가자는 일일 평균 걸음 수의 중간값에 따라 두 그룹(활동량이 많은 그룹과 적은 그룹)으로 나누어졌습니다. 그룹 간 미생물군집 풍부도와 SCFA 농도를 비교하고, 장 미생물군집 풍부도와 연구 변수 간의 상관 분석을 수행했습니다.

각 PA 그룹에서 다른 미생물군집 분류군과 덜 활동적인 개인의 분변에서 증가한 SCFA가 발견되었습니다.

걸음 수 변화는 19개의 ASV, 가족 수준에서 3개의 미생물, 속 수준에서 11개의 미생물의 상대적 풍부도와 긍정적 또는 부정적 연관성을 보였습니다. 또한 신체 활동, 장 미생물, SCFA, 수면 매개변수 간 유의미한 연관성이 발견되었습니다. 본 연구 결과는 불면증을 겪는 노인에서 신체 활동, 장 미생물 구성, 주요 대사산물 간의 관계에 대한 새로운 통찰을 제공합니다.

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Introduction

Sleep disturbances are highly preval‎ent in older adults, with insomnia the most common sleep disorder1. Insomnia is defined as a “chronic or acute sleep disorder characterized by a complaint of difficulty initiating, and/or maintaining sleep, and/or a subjective complaint of poor sleep quality that results in daytime impairment and subjective report of impairment”2. Its symptoms affect around 50% of the older adult population3 and are associated with adverse health outcomes such as systemic inflammation4 and all-cause mortality5. Numerous studies have explored the link between sleep and physical activity (PA)6. Regular PA is thought to be an efficacious, safe, and cost-saving intervention method for those who experience inadequate sleep quantity or quality6 and has been shown to improve insomnia and sleep complaints in community-dwelling older adults7,8,9.

To eval‎uate physical activity in this population, daily step count is an essential as well as easily and securely implemented10 tool; the recommended daily step count may vary according to the population characteristics11. Although recent studies recommend a dose of 7000–8000 steps per day11, a minimum of 6000 daily steps (the upper quartile of community-dwelling older adults) has been shown to improve health (i.e., reduce mortality and frailty)12,13. For older adults with insomnia, the accurate dose–response has yet to be determined and should be linked to specific and measurable outcomes such as changes in the gut microbiome composition. Indeed, studies are accumulating at present regarding the link between microbiota profile and its metabolites to PA in older adults14,15,16,17.

PA and specifically aerobic exercise training have been shown to increase intestinal Bacteroides while improving cardiorespiratory fitness in healthy older women18. Regular exercise has been shown to reshape aging-induced alterations in microbial composition and function19. By contrast, short-term endurance exercise has little effect on gut microbiota composition in older individuals. Five weeks of endurance exercise, for example, caused few changes in older men’s microbiome profile (i.e., increased Oscillospira and decreased Clostridium difficile)20. Furthermore, a combination of moderate resistance training and Bifidobacterium spp. supplementation improved cognitive function, body weight, and bowel movement frequency in healthy older adults21. Short-chain fatty acids (SCFAs), the main fermentation metabolites of dietary fibers produced by the gut microbiota, were suggested as possible pivots underlying the relationship between PA and health14.

In general, SCFAs are linked to improved gut health through several effects, including intestinal barrier integrity22, mucus production23, and serotonin release, a key regulator of gastrointestinal secretion and motility24. Interestingly, higher SCFAs in stool were associated with negative outcomes such as gut microbiome dysbiosis, obesity, hypertension, and cardiometabolic disease risk factors25. These contradictory effects may be explained by a lower absorption of metabolites from the human rectum and distal colon since their circulation levels were shown to be inversely associated with those in feces26.

In neurobiological diseases such as insomnia, SCFAs have been thought to function as mediators linking gut bacteria to mechanisms in the brain27,28. Zhang et al. showed differences in gut microbiota, serum metabolites, and serum immune factors between healthy controls and older adults with insomnia29. A recent study revealed a positive association between a more severe insomnia phenotype in older adults and higher SCFA levels in feces30. Moreover, severe insomnia was also linked to an increased inflammatory state in the gut31. This study examines the effect of physical activity on gut microbiota composition and SCFA concentrations among community-dwelling older adults with insomnia. We also suggest a potential mechanism that explains the contribution of PA to the microbiome profile and SCFA stool levels in this population.

수면 장애는 노인에서 매우 흔하며, 불면증이 가장 흔한 수면 장애입니다1. 불면증은 “수면 시작, 유지, 또는 수면 품질에 대한 주관적 불만으로 인해 주간 기능 저하와 주관적 기능 저하 보고가 동반되는 만성 또는 급성 수면 장애”로 정의됩니다2. 그 증상은 노인 인구의 약 50%에게 영향을 미치며3, 전신 염증4 및 모든 원인 사망률5와 같은 부정적인 건강 결과와 연관되어 있습니다. 수면과 신체 활동(PA) 간의 연관성을 탐구한 수많은 연구가 진행되었습니다6.

규칙적인 신체 활동은

수면 양이나 질이 부족한 사람들에게 효과적이고 안전하며 비용 효율적인 개입 방법으로 여겨지며6,

지역사회에 거주하는 노인들의 불면증과 수면 관련 불만을 개선하는 것으로 나타났습니다7,8,9.

이 인구 집단에서 신체 활동을 평가하기 위해 일일 걸음 수는 필수적이며 쉽게 안전하게 측정할 수 있는 도구입니다10; 권장 일일 걸음 수는 인구 특성11에 따라 다를 수 있습니다. 최근 연구에서는 하루 7,000~8,000 걸음11을 권장하지만, 지역사회 거주 노인 상위 4분위군의 최소 6,000 걸음은 건강 개선(즉, 사망률 및 허약함 감소)에 효과적임이 입증되었습니다12,13. 불면증을 겪는 노인에게 정확한 용량-반응 관계는 아직 확정되지 않았으며, 장내 미생물군집 구성 변화와 같은 구체적이고 측정 가능한 결과와 연계되어야 합니다. 실제로 현재 노인에서 미생물군집 프로파일과 그 대사산물과 신체 활동 간의 연관성을 탐구하는 연구가 축적되고 있습니다14,15,16,17.

PA, 특히 유산소 운동 훈련은

건강한 노인 여성의 장내 Bacteroides를 증가시키며

심폐 기능을 개선하는 것으로 나타났습니다18.

규칙적인 운동은

노화로 인한 미생물 구성과 기능의 변화를 재구성하는 것으로 나타났습니다19.

반면, 단기 내구력 운동은

노인들의 장내 미생물 구성에 거의 영향을 미치지 않습니다.

예를 들어,

5주간의 내구력 운동은

노인 남성의 미생물군집 프로파일(즉, Oscillospira 증가 및 Clostridium difficile 감소)에

거의 변화를 일으키지 않았습니다20.

또한, 중간 강도 저항 운동과 Bifidobacterium spp.

보충제의 조합은 건강한 노인들의 인지 기능, 체중, 배변 빈도를 개선했습니다21.

단쇄 지방산(SCFAs)은

장내 미생물이 식이 섬유를 발효하여 생성하는 주요 대사산물로,

신체 활동과 건강 사이의 관계에서 핵심 역할을 할 수 있는 요인으로 제안되었습니다14.

일반적으로

SCFAs는

장벽 기능 강화22, 점액 생성23, 세로토닌 분비(위장관 분비 및 운동의 주요 조절자)24 등

여러 효과를 통해 장 건강 개선과 연관되어 있습니다.

흥미롭게도,

대변 내 SCFA 수치가 높을수록

장 미생물군집 불균형, 비만, 고혈압, 심혈관 대사 질환 위험 요인과 같은

부정적인 결과와 연관되었습니다25.

이러한 상반된 효과는

인간 직장 및 원위 결장에서 대사산물의 흡수율이 낮기 때문일 수 있습니다.

실제로,

대사산물의 혈중 농도는

대변 내 농도와 역상관 관계를 보였습니다26.

신경생물학적 질환인 불면증에서 SCFAs는 장 세균과 뇌 메커니즘을 연결하는 매개체로 기능한다고 여겨져 왔습니다27,28. Zhang 등29은 건강한 대조군과 불면증을 가진 노인 사이에서 장 미생물군, 혈청 대사산물, 혈청 면역 인자에서 차이를 보여주었습니다.

최근 연구에서는

노인에서 더 심각한 불면증 증상과

대변 내 SCFA 농도 증가 사이에 긍정적인 연관성이 발견되었습니다30.

또한,

심각한 불면증은

장 내 염증 상태의 증가와도 연관되었습니다31.

본 연구는 불면증을 겪는 지역사회 거주 노인에서 신체 활동이 장내 미생물군집 구성과 SCFA 농도에 미치는 영향을 조사합니다.

또한 이 인구 집단에서 신체 활동이 미생물군집 프로파일과 SCFA 분변 농도에 기여하는 메커니즘을 설명하는 잠재적 메커니즘을 제안합니다.

Methods

The study was carried out in accordance with the Declaration of Helsinki. The institutional review board (IRB) of the Faculty of Social Welfare and Health Sciences at the University of Haifa approved this study and all its methods, conforming to relevant guidelines and regulations (approval number 026/17). All study participants signed informed consent forms. This research received funding from the Israeli Ministry of Science and Technology grant number 3-13607.

Participants

Sixty-eight community dwelling older adults with insomnia, ages 65 and older32, were recruited from community centres in Israel. All participants had no significant visual or hearing impairments, chronic pain, substantial medical, neurological, or psychiatric illness, alcohol or drug abuse, or psychiatric medication. Based on self-report, participants had no sleep apnea syndrome (SAS) or periodic limb movement disorder during sleep (PLMD). No cognitive impairment was reported according to the Mini-Mental State Examination (MMSE < 26)33. Insomnia was defined according to Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5)2, and its symptoms were diagnosed from self-report and confirmed by two weeks of actigraphic recordings based on accepted benchmark criteria (sleep onset latency [SOL]) or wake time after sleep onset (WASO) of ≥ 31 min, and < 85% sleep efficiency (SE, percentage of total sleep time after initial sleep onset) for at least three out of seven nights each week). The participants were then divided into two groups according to their median daily step counts: a more active group (step counts ≥ 6500) and a less active group (step counts < 6500). Step-count cutoff was in consonance with previous studies11,12,13 and in line with the recommendations for older adults with various morbidities11. Participants were asked to provide a fecal sample and anthropometric measures and to fill in medical history and food consumption questionnaires. Nineteen adults (16 from the less active group and three from the more active group) did not return fecal samples and were excluded from the current analysis. A flowchart summarizing the study stages can be found below.

Required sample size was calculated via G*Power (v.3.1)34 based on hypothesized group difference in number of steps and was determined to be n = 17 pre group. We assumed a two-tailed t-test, with large effect size for group difference (d = 1), α = 0.05, and 80% power to detect an effect.

Measures

Physical activity and sleep measurements

Physical activity levels, operationalized as an average daily step count, were assessed objectively over two weeks of daily measurement using a GT9X ActiGraph triaxial accelerometer (ActiGraph, Phillips Respironics, Pensacola, FL) worn on the right ankle35. Sleep recordings were performed in one-minute epochs with the Actiware 6.0.9 algorithm using the same instrument. Sleep onset and offset were set to the first and last epoch of ten consecutive immobile minutes, respectively. To establish and mark the timing of rest intervals, participants were instructed to first press on the event marker, a built-in feature of the Actiwatch, at bedtime and at final awakening, and then to complete a sleep diary each morning upon awakening. Rest intervals were based primarily on event markers and were verified by sleep diary records. Derived measures included total sleep time (TST, minutes of sleep from intended bedtime to final wake time), sleep onset latency (SOL, minutes to fall asleep from bedtime), sleep efficiency (SE, percentage of total sleep time after initial sleep onset), and wake time after sleep onset (WASO, total wake minutes after sleep onset).

Demographic, anthropometric, medical, and nutritional assessment

The participants filled in demographic, anthropometric, and medical history questionnaires. A valid Food Frequency questionnaire was administrated by a registered dietitian and used to eval‎uate the consumption of macro and micronutrients36. Consumption levels of total fibers and proteins were compared between groups.

Fecal sample analyses

Sampling

Participants received clear instructions for the collection of samples at their homes. A fecal sample of each participant was self-collected during the morning hours, stored in screw-capped collection containers filled with an RNase inhibitor solution (DNA/RNA Shield Fecal Collection Tube, Zymo Research, CA, USA), and kept at room temperature for a maximum of one week, transported at room temperature to the laboratory, and kept at − 80 °C until analysis.

DNA extraction and 16S rRNA amplification and sequencing (Illumina MiSeq Platform)37

DNA was extracted from all fecal samples using the DNeasy PowerSoil Kit (Qiagen, Hilden, Germany), according to the manufacturer’s instructions. Barcoded universal primers 515F (ACACTGACGACATGGTTCTACAGTGCCAGCMGCCGCGGT) and 806R (TACGGTAGCAGAGACTTGGTCTGGACTACHVGGGTWTCT) containing Illumina adapter sequences, which target the highly conserved V4 region, were used to amplify the microbiota from individual samples. Amplification consisted of 20 cycles of 98 °C for 10 s, annealing at 55 °C for 10 s, and 72 °C for 20 s, followed by 1 min at 72 °C. The second PCR was done using the Access Array primers for Illumina (Fluidigm) to add the adaptor and index sequences to the samples for Illumina sequencing.

Amplicons were purified using AMPure XP beads (Beckman Coulter, CA, USA) and subsequently quantified using Qubit (Life Technologies, CA, USA); size was determined by Tapestation (Agilent Technologies, CA, USA). The samples were then loaded on the Miseq (TermoFisher, MA, USA) and sequenced using the Miseq V2 (500 cycles) kit to generate paired-end reads of 2 × 250 bases.

Sequencing data analyses

Bacterial sequences were analyzed using the packages “Dada2” and “pyloseq” uploaded to the RStudio software version 4.1.038. Sequences were manually inspected and quality-filtered. A maximum length cut-off was set to 175 bp, discarding forward and reverse reads with an expected error rate higher that 3 nucleotides per 100 bp after truncation. Paired reads were merged, chimeras were removed and an ASVs table was generated.

All further analyses were made with the MicrobiomeAnalyst software platform39. Taxonomy was assigned using the Silva reference database version 135.140 and samples with at least 11,500 read counts were included in the analysis (1 sample dropped). Further filters included the removal of low count in samples (10% cut-off for preval‎ence). The data was normalized by Cumulative Sum Scaling (CSS) except for the alpha diversity analysis.

SCFA extraction

SCFA extraction and gas chromatography-mass spectrometry (GC–MS) analysis were performed as described by Magzal et al.30. Briefly, samples were thoroughly mixed for 5 min using a vortex. Their pH was then adjusted to 2–3 using orthophosphoric acid (16%v/v) and maintained at room temperature for 10 min. Samples were centrifuged at 4 °C for 5 min at 10,000 rpm, and the supernatant transferred to a vial for GC–MS analyses. 2-methyl-butyric-acid (#109,959, Sigma-Aldrich, MO, USA) was added to each vial as an internal standard at a final concentration of 0.001 M. All vials were stored at -20 °C before GC–MS analyses. The fecal sediment obtained after centrifugation was dried at 60 °C for 5–7 days, and its weight determined.

GC–MS analysis

A sample volume of 1μL was injected into a 0.25 mm × 30 m × 0.25 μm fused-silica capillary column with a free fatty acid phase (DB-FFAP 122–3232, Agilent Technologies, CA, USA) of a gas-chromatographer (Agilent 7890A, Agilent Technologies, CA, USA) equipped with an automatic liquid sampler (Gerstel, Mülheim an der Ruhr, Germany). The initial oven temperature was 70 °C, held for 0.75 min, raised to 160 °C at 5 °C/min, raised to 230 °C at 20 °C/min and held for 5.0 min, totaling 27.25 min of runtime. A glass liner with a glass wool plug at its lower end was used to avoid contamination of the GC column with nonvolatile fecal material. Detection was performed by a mass detector coupled to the system (Agilent 5975C) and was operated in the selection ionization mode (SIM). Ion selection of the SCFAs was based on the retention time of standard compounds (WSFA-4, #47056, Sigma-Aldrich, MO, USA). Chemstation software (Hewlett–Packard, CA, USA) was used for acquisition.

Statistical analyses

Statistical analyses of step numbers, demographics, sleep, dietary and health-related measures between the physical activity groups were performed using SPSS version 26 (IBM SPSS Statistics, New York, US). The comparison of gut microbiota composition between high and low PA groups was performed using the MicrobiomeAnalyst39 platform. Parametric data are presented as mean ± standard deviation, and non-parametric data are presented as frequencies (percentage and number of subjects). Demographic, anthropometric, medical, and nutritional data include age, body mass index (BMI), years of education, daily protein and fiber intake, gender, diagnoses of high blood pressure, high cholesterol, diabetes, and heart conditions, as well as uses of anti-cholinergic, depression, and sleep medication. These variables were analyzed via independent sample t-tests, Mann–Whitney U tests, Pearson’s chi-squared tests, or Fisher's exact tests. The outcome measures were the concentration (μmol per gram dry feces) of total SCFAs and of the individual SCFAs: acetate, propionate, isobutyrate, butyrate, isovalerate, and valerate. Since the distribution of each of the SCFAs violated the assumption of normality, the difference between the physical activity groups was analyzed via Mann–Whitney U tests. Data are presented as raw mean ± SD and mean rank, and we report both U and Z statistics. The Monte Carlo sampling method (200,000 samples) was used to estimate exact p-values; the reported p-values are the Monte Carlo point estimates. The effect size estimator for the significant differences was eta-squared (η2). The Shannon index was used to determine Alpha Diversity within samples and the Bray–Curtis index facilitated the analysis of phylogenetic differences between both groups (β-diversity). The algorithm metagenomesSeq (zero-inflated Gassian fit) enabled identification of microorganisms that are differently abundant between both groups. The Spearman correlation analysis were performed using the package “metagenomeseq”, RStudio software (version 4.1.0)38. The data was normalized by Cumulative Sum Scaling (CSS). Only ASVs or taxa with preval‎ence of above 10% were considered for analysis (i.e., > 5 samples).

Results

Descriptive statistics

The median daily step counts for the less active and the more active group were significantly different (5544.90, IQR = 4269.04–6239.50 and 8367.80, IQR = 7555.00–10,505.16, respectively) (Table 1). Comparison analysis of demographic, dietary and health-related variables indicated no differences between both groups in age, BMI, years of education, gender, and daily protein and fiber intake. The groups did not differ significantly in the percentage of participants diagnosed with high blood pressure, high cholesterol levels, diabetes, a heart condition, or who used anti-cholinergic, depression, and sleep medication. Likewise, no significant differences in sleep parameters were detected between groups (Table 1).

Table 1 Comparisons of step counts, demographic, sleep, and dietary and health-related measures between the physical activity groups.

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Comparison of gut microbiota composition between high and low PA groups

We eval‎uated the composition of all participants’ gut microbiota at the phylum and genus levels (Fig. 1A,B). The most abundant phyla were Firmicutes (52.14%), Bacteroidota (39.65%), followed by Proteobacteria (4.99%), Verrucomicrobia (1.98%), Actinobacteriota (0.71%) and Desulfobacterota (0.52%). At the genus level, the 6 most abundant gut microbiota were Bacteroides (27.94%), Faecalibacterium (11.61%), Prevotella 9 (5.67%) followed by UCG 002 (4.69%), Alistipes (3.30%) and Eubacterium eligens group (2.52%).

Figure 1

(A,B) Taxonomic composition distribution in samples at the phylum (A) and genus (B) levels. The 49 samples are divided into two groups, less active and more active individuals. The bacteria relative abundances are displayed in the histogram in the corresponding proportion and color according to the legend. (C,D) Boxplots showing alpha diversity the feature level (C) (Shannon index) and beta diversity at the feature level (D) (Bray–Curtis index). Plots in blue represent the data from the more-active group, plots in red are from the less-active group.

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Based on relative abundance, higher step counts did not cause changes in microbiota diversity within individuals (Shannon Index, Mann–Whitney, p = 0.493) and in microbiota divergence between individuals (Beta diversity, PERMANOVA, p < 0.394) (Fig. 1C,D).

We performed a screening of the gut microbiota at ASVs and family and genus levels between more active and less active groups using the metagenomeseq method. More active individuals had significantly higher abundances of 13 ASVs, 3 bacteria at the family level and 4 bacteria at the genus level. Seven ASVs were significant higher in less active individuals. The results are summarized in Table 2. Bifidobacterium, Clostridium sensu stricto 1, UCG-002, Catenibacterium, Peptococcus, Holdemanella and Butyricicoccus are among the genera present in more active individuals. Less active ones show a higher relative abundance of the genera Barnesiella, Blautia, Lachnoclostridium, Christensenellaceae R-7 group and UCG-005.

Table 2 Significant microbiota ASVs, families and genera present in the more active and less active groups.

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Comparison of SCFA concentrations between more and less active groups

We determined SCFA concentrations for both groups using GC–MS. Acetate was the most preval‎ent SCFA in both groups, followed by butyrate and propionate. Analysis of the difference in SCFAs (acetate, propionate, isobutyrate, butyrate, isovalerate, valerate, and total SCFAs) between the groups is presented in Table 3.

Table 3 Comparisons of SCFAs between the physical activity groups.

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The analysis revealed that the less active group had significantly higher concentrations of propionate, isobutyrate, and valerate compared to the more active group. The magnitude of the difference in concentration between the study groups was high for propionate (η2 = 16). The rest of the comparisons had a medium effect size. The less active group also had significantly higher concentrations of total fecal SCFAs, compared to the more active activity group, with a medium effect size (η2 = 08).

Correlations between the gut microbiome and physical activity

To further explore the relationship between microbial changes and physical activity, we performed correlation analyses of all the microbial flora and step counts. Changes in step counts were positively or negatively associated with the relative abundance of 19 ASVs, 3 microorganisms at the family level, and 11 microorganisms at the genus level. In addition, we performed the same correlation analysis of the gut microbiota with the parameters age, BMI, SCFAs, sleep efficiency, and the nutrients protein and fiber. Significant associations are shown in Table 4 and a complete table with all correlations tested can be found in the supplementary Table S1. The family Monoglobaceae and its genus Monoglobus were negatively correlated to step counts (p = 0.031) and SOL (p = 0.049) and positively associated with isobutyrate concentrations (p = 0.039) and sleep efficiency (p = 0.022). The genus Ruminococcus was negatively correlated to physical activity (p = 0.039) and fiber intake (p = 0.002) but positively associated with isovalerate (p = 0.012). Anaerovoracaceae FamilyXIII UCG-001 was positively associated with PA (p = 0.021) but negatively associated with propionate (p = 0.036). Positive correlations were found between the genus UCG-002, PA (p = 0.009), and protein intake (p = 0.037). The genus Lachnoclostridium was negatively correlated with PA (p = 0.017) and protein intake (p = 0.020). The genus Sutterella was positively associated with PA (p = 0.047) and negatively correlated to the SCFAs propionate (p = 0.028), and isobutyrate (p = 0.032). Bacteroides ovatus, Bacteroides clarus, and Bacteroides uniformis were negatively correlated with PA (p = 0.000, p = 0.010, and p = 0.044, respectively). Bacteroides putredinis and Blautia hansenii correlated oppositely with PA (S.corr = -0.333, p = 0.021 and S.corr = 0.332, p = 0.047, respectively) and BMI (S.corr = 0.0331, p = 0.020 and S.corr = -0.249, p = 0.039, respectively).

Table 4 Significant Spearman positive and negative correlations among the gut microbiome, physical activity (step counts), and measured parameters.

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Other positive correlations with PA include the taxa Catenibacterium (p = 0.01), Lachnospira (p = 0.017), Clostridium sensu stricto 1 (p = 0.013), Dialister (p = 0.017), Winogradskyella (p = 0.043), Epibacterium (p = 0.041), SAR86 clade (p = 0.048), [Eubacterium] coprostanoligenes group (p = 0.043), Clostridia vadinBB60 group (p = 0.014) and Clostridia UCG-014 (p = 0.015). Negative correlations of PA were found with the taxa Lachnospiraceae (p = 0.011), Marinifilaceae (p = 0.045), DTU089 (p = 0.041), Alistipes (p = 0.037), Butyricimonas (p = 0.033), and Negativibacillus (p = 0.025).

Correlations between measured variables

Additional correlations were carried out between PA, age, BMI, SCFAs, sleep efficiency, fiber and protein intakes. Correlation results are shown in Fig. 2A and p-value ranges are shown in Fig. 2B. Physical activity was negatively correlated to BMI, propionate, and valerate (p = 0.022, p = 0.022, and p = 0.021, respectively). Age was positively correlated to isovalerate (p = 0.033). SCFAs propionate, butyrate, isovalerate and valerate (p = 0.008, p = 0.040, p = 0.014 and p = 0.029, respectively) correlated positively to BMI. Sleep efficiency was correlated to acetate, butyrate, and total SCFAs (p = 0.001, p = 0.002, and p = 0.009, respectively).

Figure 2

Spearman correlations between measured variables (A). All variables are shown in the X and Y-axis. Values of significant correlations are shown in the correspondent squares. P-values of measured variables (B). All variables are shown in the X and Y-axis. Color-filled squares represent p-values ≤ 0.05.

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Discussion

We aimed to determine differences in the gut microbiota composition and its primary metabolites between more active and less active groups of older adults with insomnia and unveil potential connections between specific microbiome taxa and measured variables. The results show different microbiota taxa in each PA group, increased SCFAs in feces of less active individuals, and significant associations between physical activity, gut microbiota, SCFAs, and sleep parameters.

In the current study, the microbial family Erysipelotrichaceae and Peptococcaceae and the genus Peptococcus were significantly higher in the more active group (> 6,500 steps/day). Similar results were obtained by studies that eval‎uated PA by self-reported questionnaires41,42,43. For example, increased Erysipelotrichaceae were shown in individuals that reported frequent exercise41 and among adults with higher cardiorespiratory fitness42. Furthermore, Erysipelotrichaceae abundances were significantly higher in elite athletes when compared to controls43 and in a healthy-aging cohort when compared with non-healthy counterparts44. More importantly, these families were also positively associated with higher step counts in community-dwelling older men12.

At a genus level, we show positive correlations between Catenibacterium, a member of the Erysipelotrichaceae family, and step counts. Catenibacterium was also enriched in more active South Korean subjects45 and more abundant in top Polish endurance athletes than sedentary controls46. In addition, more active individuals were characterized by higher abundances of Prevotella 9, Clostridium sensu stricto 1, and UCG-002; the latter two have also been positively correlated to PA. These results are consistent with previous work showing higher levels of these taxa in athletes46. For more sedentary groups, the results are contradictory. We show higher abundances of Lachnoclostridium, Blautia, Barnesiella, and UCG-005 in less active individuals, opposing previous publications46,47,48.

Several mechanisms have been proposed to explain the interrelationship between physical activity and microbiota composition, such as antioxidant activity, immune modulation, gastrointestinal permeability, and the production of metabolites such as SCFAs49. Although SCFAs can influence a wide array of host systems and metabolic pathways50,51, the mechanism by which PA modulate SCFAs and vice-versa remains speculative51. Furthermore, associations between PA and SCFAs have not been extensively studied. Existing publications focused mainly on the effects of exercise interventions on healthy young adults, and the results shown are inconsistent14,42,52,53. In the present study, more active individuals had significantly lower levels of fecal SCFAs despite similar demographics, health characteristics, sleep parameters, and recorded fiber and protein intake between groups. Correlation analysis showed negative associations of step-counts with SCFAs propionate and valerate. Surprisingly, [Ruminococcus] gauvreauii group, a member of the Lachnospiraceae family and a SCFAs producer54, was abundant in the more active group but correlated negatively to propionate and valerate. Other gut microbiota members such as the genus Monoglobus and Ruminococcus correlated negatively to PA and were positively associated with isobutyrate and isovalerate levels, respectively.

Although all participants were older adults with insomnia, we examined the associations between sleep parameters, SCFAs, microbiota, and PA. SE was negatively associated with SCFAs (acetate and butyrate) and total SCFAs. Genus Monoglobus, associated with PA and isobutyrate, was positively associated with SE and negatively associated with SOL. These results may imply the existence of a link between physical activity, gut microbiota, SCFAs, and sleep. To the best of our knowledge, this is the first study to show associations between gut microbiota, SCFA concentrations, and sleep quality in older individuals.

Other important results include positive associations between BMI and SCFAs butyrate, propionate, isovalerate and valerate. BMI was inversely correlated with PA and with the genera [Ruminococcus] gauvreauii group and Holdemanella, both abundant in the more active group. The relationship between BMI gut microbiota and SCFAs produced by intestinal bacteria is not yet fully understood55, but SCFAs represent an important energy source for the human body, contributing to lipogenesis and accumulation in adipocytes, leading to energy harvest56. Moreover, higher fecal SCFA concentrations may be associated with gut dysbiosis, gut permeability, excess adiposity, and cardiometabolic risk factors57.

This study has some limitations. The sample is relatively small and homogenous; however, since this is the first study to explore community-dwelling older adults with insomnia, the similarity between the groups enables us to extract the effect of physical activity with fewer biases. Indeed, physical activity can be quantified in many ways and step count is not the sole and or necessarily the ultimate representation of PA; however, this consistent and objective measure gains greater validity due to its accuracy and relevance to older adults’ daily life. Additionally, since there is no agreed-upon recommendation for daily step count for older adults, we used large population-based studies11 to support the cut-off we used; future studies should explore a variety of doses for various sub-groups with different health characteristics.

Conclusion

In conclusion, physical activity is undoubtedly associated with positive outcomes. However, the role of the gut microbiota composition and its metabolites as modulators is still poorly understood and requires further research. Our findings add to the existing literature in elucidating the mechanism underlying these relationships in older adult populations with insomnia.

논의

우리는

불면증을 겪는 노인 중 활동량이 많은 그룹과 적은 그룹 간의 장내 미생물군집 구성 및 주요 대사산물 차이를 확인하고,

특정 미생물군집 분류군과 측정 변수 간의 잠재적 연관성을 규명하는 것을 목표로 했습니다.

결과는 각 신체 활동 그룹에서 다른 미생물군집 분류군이 관찰되었으며,

활동량이 적은 개인의 분변에서 단쇄지방산(SCFAs)이 증가했고,

신체 활동, 장내 미생물군집, SCFAs, 수면 매개변수 간 유의미한 연관성이 확인되었습니다.

본 연구에서 미생물 가족 Erysipelotrichaceae와 Peptococcaceae 및 속 Peptococcus는 더 활동적인 그룹(> 6,500 걸음/일)에서 유의미하게 높게 나타났습니다. 자기 보고식 설문지를 통해 신체 활동을 평가한 연구들41,42,43에서도 유사한 결과가 관찰되었습니다. 예를 들어, 자주 운동을 한다고 보고한 개인에서 Erysipelotrichaceae가 증가했으며41, 심폐 기능이 높은 성인에서도 마찬가지였습니다42.

또한, 엘리트 운동선수에서 대조군에 비해 Erysipelotrichaceae의 풍부도가 유의미하게 높았으며43, 건강한 노화 코호트에서 비건강군에 비해 높았습니다44. 더욱 중요한 점은 이 가족들이 지역사회 거주 노인 남성에서 걸음 수와 긍정적으로 연관되었다는 점입니다12.

속 수준에서, 우리는 Erysipelotrichaceae 과에 속하는 Catenibacterium과 걸음 수 사이의 양의 상관관계를 보여주었습니다. Catenibacterium은 활동적인 한국인 대상군에서 풍부하게 발견되었으며45, 폴란드의 최상위 내구력 운동선수에서 비활동적 대조군보다 풍부했습니다46. 또한, 활동적인 개인은 Prevotella 9, Clostridium sensu stricto 1, 및 UCG-002의 풍부도가 높았으며, 후자 두 종은 신체 활동(PA)과도 양의 상관관계를 보였습니다. 이 결과는 운동선수에서 이러한 세균군의 수준이 높다는 이전 연구 결과와 일치합니다46. 활동량이 적은 그룹에서는 결과가 모순됩니다. 우리는 활동량이 적은 개인에서 Lachnoclostridium, Blautia, Barnesiella 및 UCG-005의 풍부도가 더 높다는 것을 보여주었으며, 이는 이전 연구 결과46,47,48와 반대됩니다.

신체 활동과 미생물군집 구성 간의 상호작용을 설명하기 위해 항산화 활동, 면역 조절, 위장관 투과성, SCFAs와 같은 대사산물 생산 등이 제안되었습니다49. SCFAs는 호스트 시스템과 대사 경로에 광범위한 영향을 미칠 수 있지만50,51, PA가 SCFAs를 조절하는 메커니즘은 여전히 추측 단계에 머물러 있습니다51. 또한 PA와 SCFAs 간의 연관성은 충분히 연구되지 않았습니다. 기존 연구는 주로 건강한 젊은 성인에 대한 운동 개입의 효과를 중점적으로 다루었으며, 결과는 일관되지 않았습니다14,42,52,53. 본 연구에서 더 활동적인 개인은 그룹 간 인구 통계학적 특성, 건강 특성, 수면 매개변수, 기록된 식이 섬유 및 단백질 섭취량이 유사함에도 불구하고 분변 SCFAs 수치가 유의미하게 낮았습니다. 상관 분석 결과, 걸음 수와 SCFAs 프로피오네이트 및 발레레이트 사이에 음의 상관관계가 관찰되었습니다. 놀랍게도, Lachnospiraceae 과에 속하며 SCFA 생산균으로 알려진 [Ruminococcus] gauvreauii 그룹은 활동량이 많은 그룹에서 풍부했지만 프로피오네이트와 발레레이트와 음의 상관관계를 보였습니다. 다른 장내 미생물군 구성원인 Monoglobus와 Ruminococcus 속은 신체 활동량과 음의 상관관계를 보였으며, 각각 이소부티레이트와 이소발레레이트 수준과 양의 상관관계를 나타냈습니다.

모든 참가자는 불면증을 겪는 노인でしたが, 수면 매개변수, SCFAs, 미생물군집, 및 PA 간의 연관성을 분석했습니다. SE는 SCFAs(아세테이트와 부티레이트) 및 총 SCFAs와 음의 상관관계를 보였습니다. PA와 이소부티레이트와 연관된 Monoglobus 속은 SE와 양의 상관관계를, SOL과 음의 상관관계를 보였습니다. 이 결과는 신체 활동, 장내 미생물군집, SCFAs, 및 수면 간의 연관성이 존재할 수 있음을 시사합니다. 우리의 지식 범위 내에서, 이는 노인에서 장 미생물군, SCFA 농도, 수면 품질 간의 연관성을 보여주는 첫 번째 연구입니다.

다른 중요한 결과에는 BMI와 SCFA(부티레이트, 프로피오네이트, 이소발레이트, 발레이트) 간의 양의 연관성이 포함됩니다. BMI는 신체 활동과 [Ruminococcus] gauvreauii 그룹 및 Holdemanella 속과 역상관 관계를 보였으며, 이 두 속은 활동량이 많은 그룹에서 풍부했습니다. BMI, 장내 미생물군집, 장내 세균이 생성하는 SCFA 간의 관계는 아직 완전히 이해되지 않았습니다55. 그러나 SCFA는 인체에 중요한 에너지 원천으로, 지방 생성 및 지방 세포 내 축적에 기여하여 에너지 획득에 기여합니다56. 또한, 분변 SCFA 농도가 높을수록 장내 미생물 불균형, 장 투과성, 과도한 지방 축적, 심혈관 대사 위험 요인과 연관될 수 있습니다57.

이 연구에는 몇 가지 한계가 있습니다. 표본 크기가 상대적으로 작고 동질적이지만, 이는 불면증을 가진 지역사회 거주 노인들을 대상으로 한 첫 번째 연구이기 때문에 그룹 간의 유사성이 신체 활동의 효과를 편향 없이 추출하는 데 도움이 됩니다. 실제로 신체 활동은 다양한 방법으로 측정될 수 있으며, 걸음 수는 신체 활동의 유일한 또는 최종적인 표현이 아닙니다. 그러나 이 측정 방법은 정확성과 노인들의 일상 생활과의 관련성으로 인해 일관성과 객관성을 갖추어 더 높은 타당성을 갖습니다. 또한 노인에 대한 일일 걸음 수에 대한 합의된 권장치가 없기 때문에, 우리는 사용한 기준점을 지원하기 위해 대규모 인구 기반 연구11를 참고했습니다. 향후 연구에서는 다양한 건강 특성을 가진 하위 그룹에 대한 다양한 용량을 탐구해야 합니다.

결론

결론적으로, 신체 활동은 긍정적인 결과와 분명히 연관되어 있습니다. 그러나 장내 미생물 구성과 그 대사산물이 조절자로서의 역할은 여전히 잘 이해되지 않으며 추가 연구가 필요합니다. 본 연구 결과는 불면증을 가진 노인 인구에서 이러한 관계의 메커니즘을 밝히는 데 기존 문헌에 기여합니다.

Data availability

The datasets generated and/or analysed during the current study are available in the NCBI-SRA repository: https://dataview.ncbi.nlm.nih.gov/object/PRJNA730721?reviewer=tn09eubuh30u1rqjujd21o8454. The raw SCFA data and actigraph measures for each participant and which was used for the analysis are available from the corresponding author upon request.

References

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