Gut microbiota composition correlates with diet and health …

ARTICLE RESEARCH

microbiota along the full range of the PC1 axis in the un-weighted UniFrac PCoA for long-stay-only subjects was associated with inflammation (CRP increase of 13.9 mg l 2 1 ), and other inflammatory markers significantly correlated with microbiota (IL-6 and IL-8, whole cohort). As expected, there was minimal variability amongst community-dwelling subjects, but within the long-stay subjects the most significant associations were related to functional independence (FIM), Barthel index and nutrition (MNA), followed by blood pres- sure and calf circumference. The latter may be attributable to the influence of diet and/or the microbiota on muscle mass, sarcopaenia 31 and thereby on frailty. This was supported by investigation of linkage between frailty and faecal metabolites (probabilistic principal compo- nents and covariates analysis; PPCCA 32 ). Thus, the FIM and Barthel indices were significant covariates with the faecal water metabolome (Supplementary Fig. 10) and levels of acetate, butyrate and propionate increased with higher values of both indices (that is, less frail subjects). Among community-dwelling subjects, there was also a strong asso- ciation between microbial composition and nutrition (MNA) and a weaker link with blood pressure, for which a relationship with the microbiota has previously been established 33 . There was no correla- tion between the Bacteroidetes : Firmicutes ratio and body mass index (BMI), although there was a correlation with overall microbiota in long-stay subjects. Measures for the geriatric depression test (GDT) showed significant microbiota association with PCoA axis 2 (Table 1). We detected no significant confounding of microbiota–health corre- lations due to medications, antibiotic treatment (before the 1-month exclusion window), and diet–health correlations separate from dietary impact on microbiota (Supplementary Notes). Taken together, the major trends in the microbiota that separated healthy community subjects from less healthy long-stay subjects were

genera associated include Ruminococcus and Butyricicoccus for butyrate production (Supplementary Fig. 6), but require validation in larger cohorts. Microbiota function deduced from the metagenome thus corresponded to the measured metabolome for at least one key metabolite that can affect health 25 . Microbiota–health correlations Markers of inflammation (serum TNF- a , IL-6 and IL-8 and C-reactive protein (CRP)) had significantly higher levels in long-stay and rehabilitation subjects than in community dwellers (Supplemen- tary Fig. 9). Long-stay subjects also scored poorly for diverse health parameters (Supplementary Tables 6 and 7), including the Charlson co- morbidity index (CCI, a robust predictor of survival encompassing 19 medical conditions 26 ), the geriatric depression test (GDT), the Barthel index 27 , functional independence measure (FIM 28 ), mini-mental state exam(MMSE 29 ) and mini nutritional assessment (MNA 30 ). Correlations between health parameters and microbiota composi- tion were examined using quantile (median) regression tests, adjusted for gender, age and community setting with an additive model (Sup- plementary Methods). Median regression gives less weight to extreme values than the linear regression based on ordinary least squares and consequently, is less influenced by outliers. The model was adjusted for medications that might influence the tested parameters (Supplemen- tary Table 8). The effect of medication was generally small (Supplemen- tary Table 8). Because ethnicity was exclusively Irish Caucasian it did not require model adjustment. The microbiota composition did not differ for males and females after adjusting for age and location. Significant associations between several health/frailty measure- ments and the major separations from microbiota UniFrac analysis (Fig. 1) are shown in Table 1. For example, a positive change in

Table 1 | Regression tests of associations between clinical measurements and microbiota composition. a Unweighted UniFrac PCoA for all four residence locations

Parameter

PC1

PC2

PC3

RCrange

RCs.d.

RCrange

RCs.d.

RCrange

RCs.d.

GDT

–0.42

–0.11

0.6

–2.7

–0.54 –2.02 –1.43 –0.58

0.037 0.033

0.18

0.04

0.84

Diastolic blood pressure

0.97

0.25

0.81

–10.1

–14.2

–3.1 –7.2 –0.7

0.001

Weight

–14.6

–3.8

0.033 0.022 0.006

–7.16

0.27 0.19

–1.57

0.18

–3.9

–1.01

–2.9

–3.2

0.047

IL-6 IL-8

6.71 4.23

1.7 1.1

6.1

1.22

0.007

2.08 4.06

0.45 0.89

0.2

0.43 0.31

13.6

2.7

0.03 0.72

0.47376716

TNF- a

1.1

0.28

0.62

0.13

3.9

0.9

0.0005

b Unweighted UniFrac PCoA for community-only subjects

Parameter

PC1

PC2

PC3

RCrange

RCs.d.

RCrange

RCs.d.

RCrange

RCs.d.

MNA

–1.1

–0.26 –1.98 –0.03

0.29 0.08

1.9

0.5 3.4

0.006 0.035

0.7

0.14

0.59 0.13

Diastolic blood pressure –8.4

14.3 –1.5

–15.72

–3.26 –0.16

GDT

–0.13

0.8

–0.35

0.02

–0.8

0.4

c Unweighted UniFrac PCoA for long-stay-only subjects

Parameter

PC1

PC2

PC3

RCrange

RCs.d.

RCrange

RCs.d.

RCrange

RCs.d.

Barthel

–6

–1.5 –7.8

0.004 0.046

–4.8

–1.3 –4.7 –4.8

0.036 0.024 0.009 0.004 0.047

–0.6

–0.15

0.71 0.86 0.63 0.99 0.92 0.82

FIM

–30.8

–33.3 –18.4 –11.2

–2.42

–0.6

MMSE

–12.15

–3.08 –0.98 –0.31

0.14 0.23 0.69 0.93

3.22

0.8

MNA

–3.87

–3

–0.02 –0.24

–0.005

BMI

–1.2

–5

–1.3

–0.06

0.2

0.05

–6.8

–1.77 –3.24 –0.41

0.0016

0.45

0.11

Diastolic blood pressure Systolic blood pressure

19.3 36.5 –3.2

4.9 9.3

0.015 0.007

–12.4

0.034

–15.4

–3.81 –0.51 –0.61

0.007

–1.57

0.83

–2.05 –2.48

0.87 0.72 0.93

Weight

–0.81 –0.65

0.69 0.78 0.02

–12.7

–3.3

0.024 0.006

IL-8 CRP

–2.56

22.31 –3.01

5.84

1.14

0.28

13.9 0.61 Quantile (median) regression tests of associations between clinical measurements and microbiota composition as measured by unweighted UniFrac PCoA across all four residence locations (that is, all subjects ( a ), community-only subjects ( b ) and long-stay-only subjects ( c )). Column headings are: RC range, regression coefficients scaled to the full variation along each PCoA axis, thus indicating relative magnitude and direction of the health association; RC s.d., regression coefficients scaled to one standard deviation; P , quantile regression P values generated by boot-strap analysis. Significant associations are in bold. An additive model was used to adjust for the effects of age, sex, residence location, relevant medication and the two other principal coordinates. CC, calf circumference; IL, interleukin; MMSE, mini-mental state examination. 3.53 –0.8 0.27 –2.54 –0.63

9 AUGUST 2012 | VOL 488 | NATURE | 181