Participant characteristics and PhenoAgeAccel

Depending on the availability of key variables required for this study, data from 952 of 1032 women were analysed from the S-PRESTO cohort. PhenoAge of each participant was calculated based on CA and nine biomarkers (Additional file 1: Table S1 and Methods). Subsequently, PhenoAgeAccel was calculated as the residuals resulting from the regression of PhenoAge on CA. Clinical characteristics of all participants in this study are shown in Table 1. A flow chart outlining the steps for sample selection and data analysis is provided in Fig. 1A. The association with PhenoAgeAccel was investigated through the integration of clinical measurements, blood biomarkers, and multi-omics (Fig. 1B). In this study, the majority of the participants were Chinese (72%), with 63% having a university-level education and 65% being nulliparous. The mean PhenoAge (Mean ± SD: 26.91 ± 6.68) was lower than the mean chronological age (31.34 ± 3.72), with a strong correlation (R = 0.52, Fig. 2A and Additional file 1: Fig. S1A). PhenoAgeAccel was fairly normally distributed (0.00 ± 5.69, Fig. 2B) with some outliers in the positive (older) direction. Among all the participants, 427 (45%) exhibited an accelerated biological aging (positive PhenoAgeAccel), while 525 (55%) displayed a decelerated biological aging (negative PhenoAgeAccel). PhenoAgeAccel was slightly higher in older women (0.23 ± 4.94, N = 140, aged ≥ 35 years) compared to younger women (-0.04 ± 5.81, N = 812, aged < 35 years), but the group difference was not statistically significant (Additional file 1: Fig. S1B).

Table 1 Clinical characteristics of the participants in this studyFig. 2

PhenoAgeAccel and its association results. A Phenotypic age vs. chronological age. B Histogram of PhenoAgeAccel. C-E Boxplots of ethnicity, educational attainment and parity with PhenoAgeAccel. F PhenoAgeAccel vs. BMI. G Effect size plot of 40 aging-related factors derived from multivariate analysis of clinical measurements and blood biomarkers with a nominal p-value < 0.05. *: Padj < 0.05

Compared to Chinese women, Malay had a younger CA and a similar PhenoAge (Additional file 1: Fig. S1C), but a higher PhenoAgeAccel (Fig. 2C). Indian women had the highest PhenoAge (Additional file 1: Fig. S1C) and PhenoAgeAccel (Fig. 2C) amongst all the ethnicities. Higher educational attainment was associated with lower PhenoAgeAccel (Table 1 and Fig. 2D). Multiparous women (Mean ± SD: 0.21 ± 5.82) had a higher PhenoAgeAccel than nulliparous women (-0.10 ± 5.63), but the association was not strong (Table 1 and Fig. 2E). There was a strong positive association between BMI and PhenoAgeAccel (Fig. 2F). Specifically, PhenoAge was accelerated by 2.50 years per SD increase in BMI. Notably, this effect was mainly driven by variation in weight, as height was not associated with PhenoAgeAccel (Table 1). To account for the effects of potential confounding factors, chronological age, ethnicity, educational attainment, parity, and BMI were adjusted for further analysis.

Clinical factors associated with PhenoAgeAccel

Both univariate and multivariate analyses of clinical measurements (i.e. adiposity, blood pressure, pregnant status within 12 months after recruitment, mental health, and lifestyle) were performed on PhenoAgeAccel (Additional file 1: Table S2). Effect size β was reported as years of age acceleration per SD change for continuous predictors or difference in years of age acceleration between groups for categorical predictors.

As for adiposity factors, fat mass (β = 1.86 and P = 3.61E-03), visceral adipose tissue (β = 1.69 and P = 1.08E-04) and liver fat (β = 0.67 and P = 3.45E-02) displayed a strong positive association with PhenoAgeAccel even after adjusting for covariates (Fig. 2G). Waist to hip ratio, fat free mass, subcutaneous adipose tissue, pancreatic fat, intramyocellular fat (tibialis anterior), intramyocellular fat (soleus) and blood pressure only exhibited a strong association in univariate analysis, but lost significance after adjusting for covariates. This study also explored the association between PhenoAgeAccel and mental health using the EPDS and STAI scores. No strong association was observed. The association of fertility with PhenoAgeAccel revealed that the women who conceived within 12 months after recruitment had lower PhenoAgeAccel (-0.51 ± 5.58) compared to those who did not conceive within the same timeframe (0.43 ± 5.76). However, this association was only significant in univariate analysis (β = -0.94 and P = 1.22E-02). Women who were smokers had higher PhenoAgeAccel (β = 1.99 and P = 8.12E-04) compared to non-smokers, while women who were alcohol drinkers had lower PhenoAgeAccel (β = -1.07 and P = 7.37E-03) compared to non-drinkers in univariate analysis, but the associations lost significance after adjusting for covariates. There was no observed association of PhenoAgeAccel with physical activity, sleep duration and sleep quality. A higher HEI score was associated with lower PhenoAgeAccel (β = -0.45 and P = 8.39E-03, Fig. 2G).

Blood biomarkers associated with PhenoAgeAccel

In this study, we investigated the association between PhenoAgeAccel and a wide range of biomarkers measured in serum or plasma (Additional file 1: Table S2). For glycaemic traits, since fasting glucose was one of the biomarkers included in the calculation of PhenoAge and contributed positively to it (Additional file 1: Table S1), higher levels of 2-h post-load glucose, fasting insulin, HbA1c and HOMA-IR were associated with accelerated biological aging, as expected. Serum triglyceride (β = 0.97 and P = 1.42E-07), LDL-cholesterol (β = 0.42 and P = 2.18E-02), liver enzymes (ALT: β = 0.89 and P = 1.47E-06 and AST: β = 0.58 and P = 1.52E-03 and GGT: β = 1.22 and P = 3.06E-11) showed a positive association with PhenoAgeAccel, while serum HDL-cholesterol had an inverse association with PhenoAgeAccel (β = -1.49 and P = 2.63E-16) in univariate analysis; however, these associations were not significant after adjusting for covariates.

Out of the ten vitamins, pyridoxal-5-phosphate (β = -0.51 and P = 3.43E-03) and all-trans retinol (β = -0.79 and P = 3.09E-06) were inversely associated with PhenoAgeAccel while thiamine (β = 0.38 and P = 2.33E-02), riboflavin (β = 0.43 and P = 1.08E-02) and nicotinamide (β = 0.57 and P = 8.81E-04) showed a positive association with PhenoAgeAccel (Additional file 1: Table S2). Vitamin B12, folate and vitamin D showed a significant inverse association with PhenoAgeAccel only in univariate analysis. No strong association was observed between alpha/gamma tocopherol and PhenoAgeAccel. Among the seven plasma one-carbon pathway metabolites, lower betaine (β = -0.42 and P = 1.25E-02) and higher choline (β = 0.36 and P = 3.96E-02) and homocysteine (β = 0.35 and P = 3.94E-02) levels were associated with higher PhenoAgeAccel (Additional file 1: Table S2). No association was observed for methionine. Dimethylglycine, cystathionine, and cysteine showed a positive association with PhenoAgeAccel only in univariate analysis. Association of plasma metabolites in tryptophan metabolism with PhenoAgeAccel was studied (Additional file 1: Table S2). Tryptophan (β = -0.45 and P = 8.25E-03) showed an inverse association with PhenoAgeAccel. However, kynurenine (β = 0.92 and P = 1.65E-07), 3-hydroxykynurenine (β = 0.89 and P = 1.47E-07), 3-hydroxyanthranilic acid (β = 1.04 and P = 3.80E-09), quinolinic acid (β = 1.08 and P = 1.61E-09), and neopterin (β = 1.52 and P = 3.92E-20) exhibited a positive association with PhenoAgeAccel. There was no observed association for xanthurenic acid.

Associations of PhenoAgeAccel with plasma growth hormone and insulin-like growth factors were investigated (Additional file 1: Table S2). IGF-1 (β = -0.68 and P = 1.61E-04) and IGF-2 (β = -0.62 and P = 5.04E-04) exhibited an inverse association with PhenoAgeAccel while IGFBP-1 (β = 0.54 and P = 1.39E-02), IGFBP-3 (β = 0.43 and P = 1.34E-02) and IGFBP-6 (β = 0.48 and P = 7.04E-03) showed a positive association with PhenoAgeAccel. Among the plasma fatty acids, total FAs (β = -0.35 and P = 4.33E-02), degree of unsaturation (β = -0.44 and P = 1.52E-02), PUFAs (β = -0.45 and P = 1.74E-02), LA (β = -0.55 and P = 1.21E-03) and DHA (β = -0.41 and P = 2.94E-02) showed an inverse association with PhenoAgeAccel, whereas SFAs (β = 0.45 and P = 7.94E-03) exhibited a positive association with PhenoAgeAccel (Additional file 1: Table S2). Out of the plasma amino acids and proteins, higher plasma glutamine (β = -0.72 and P = 1.31E-05), histidine (β = -0.49 and P = 3.02E-03), leucine (β = -0.38 and P = 3.69E-02), and apolipoprotein B (ApoB) (β = -0.61 and P = 4.88E-04) concentrations were associated with lower PhenoAgeAccel, while higher plasma phenylalanine (β = 0.41 and P = 1.83E-02), tyrosine (β = 0.43 and P = 2.63E-02) and GlycA (β = 1.02 and P = 7.44E-07) concentrations were associated with higher PhenoAgeAccel (Additional file 1: Table S2). Overall, plasma neopterin displayed the strongest positive association (β = 1.52) with PhenoAgeAccel, while plasma all-trans retinol demonstrated the strongest inverse association (β = -0.79) in these 70 studied biomarkers.

In total, 40 aging-related factors (P < 0.05) were derived from clinical measurements and blood biomarkers and are illustrated in Fig. 2G. Among them, 30 showed a strong association with a more stringent Padj < 0.05.

Multi-omics studies of PhenoAgeAccelPlasma lipid signatures linked with PhenoAgeAccel

Association of plasma lipidomic profile with PhenoAgeAccel was investigated after the adjustment of age, ethnicity, educational attainment, parity and BMI. Out of 689 lipid species in 36 lipid classes, 132 showed significance with a nominal p-value threshold (P < 0.05), and 16 demonstrated a strong association with a more stringent Padj < 0.05 (Additional file 2: Table S3 and Fig. 3A-B).

Fig. 3

Lipidomics and GWAS results of PhenoAgeAccel. A Forest plot of lipidomics results. Diamand: P ≥ 0.05, circle: P < 0.05 and square: Padj < 0.05. Full names of lipid classes are provided in Additional file 2: Table S3. B Volcano plots of lipidomics results. Lipid species with Padj < 0.05 are labelled. C Manhattan plot of the GWAS results. The top 3 mapped genes are labelled

For neutral lipids, higher concentrations of acylcarnitines (AC(16:0)), triacylglycerol (TG(48:0) and TG(50:0)) and diacylglycerol (DG(16:0_16:0)) with even-chain and saturated fatty acids were associated with higher PhenoAgeAccel, whereas odd-chain (AC(15:0), TG(O-54:4) [NL-17:1]) and unsaturated fatty acids (DG(18:2_18:2), TG(52:4), TG(54:6) and TG(O-54:4) [NL-18:2]) were associated with lower PhenoAgeAccel (Fig. 3A). Six cholesterol ester species were inversely associated with PhenoAgeAccel regardless of chain length or unsaturation degree. Amongst these, CE(24:5) showed the strongest association (P = 1.36E-03) with 0.93% decrease in concentration per year increase of age acceleration (Additional file 2: Table S3).

Out of the 32 aging-associated sphingolipids, 26 from ceramide (Cer), deoxy-ceramide (DeoxyCer), dihydroceramide (dhCer), sphingosine-1-phosphate (S1P), sphingosine (Sph), sphingomyelin (SM) and mono/di/trihexosylceramide (HexCer, Hex2Cer and Hex3Cer) lipid classes displayed a positive association with PhenoAgeAccel while only six lipid species (Cer(d19:1/24:0), Cer(d20:1/23:0), Cer1P(d18:1/16:0), GM3(d18:1/18:0), HexCer(d18:2/22:0)) and Hex3Cer(d18:1/18:0) showed an inverse association with it (Additional file 2: Table S3 and Fig. 3A). Amongst these, 7 lipids showed a strong association with Padj < 0.05 (Fig. 3B). Cer(d18:1/18:0) exhibited the strongest association (P = 3.77E-05) with 0.93% increase in concentration per year increase of age acceleration.

For aging-associated phospholipids (PL), 51 lipid species from phosphatidylcholine (PC), alkylphosphatidylcholine (PC(O)), phosphatidylethanolamine (PE), alkyl/alkenylphosphatidylethanolamine (PE(O) and PE(P)), phosphatidylinositol (PI) and phosphatidylserine (PS) showed a positive association with PhenoAgeAccel. Conversely, 11 lipid species, including phosphatidylcholine (PC), alkenylphosphatidylcholine (PC(P)) and alkenylphosphatidylethanolamine (PE(P)) with odd-chain (PC(17:0_18:2), PC(17:1_18:2), PC(P-35:2), PC(P-17:0/20:4) and PE(P-17:0/22:6)) and branched-chain structures (PC(15-MHDA_22:6)), as well as certain polyunsaturated fatty acids (PC(16:1_22:6), PC(18:0_22:6), (PC(40:7) and PC(P-38:5)), displayed an inverse association with PhenoAgeAccel (Additional file 2: Table S3 and Fig. 3A-B). Amongst these, 5 phospholipids showed a strong association with Padj < 0.05 (Fig. 3B). PC(O-36:0) showed the strongest positive association (P = 2.03E-05) by 1.26% increase in concentration per year increase of age acceleration, while PC(40:7) showed the strongest inverse association (P = 1.67E-03) by 0.61% decrease in concentration per year increase of age acceleration.

Among the 20 aging-associated lysophospholipids, higher concentrations of lipid species from lysoalkylphosphatidylcholine (LPC(O)), lysophosphatidylethanolamine (LPE), lysophosphatidylinositol (LPI) and lysophosphatidylserine (LPS) were associated with higher PhenoAgeAccel, whereas higher concentrations of lysophosphatidylcholine (LPC) containing odd-chain (LPC(17:0), LPC(17:1) and LPC(19:0)) and branched-chain fatty acids (LPC(15-MHDA)) were associated with lower PhenoAgeAccel. Among those, four lysophospholipids showed a strong association with Padj < 0.05 (Fig. 3B). Overall, LPI(18:1) showed the strongest positive association (P = 3.82E-05) by 1.10% increase in concentration per year increase of age acceleration while LPC(19:0) displayed the strongest inverse association (P = 3.73E-3) by 0.69% decrease in concentration per year increase of age acceleration.

Genome-wide association study of PhenoAgeAccel

A genome-wide association study (GWAS) of PhenoAgeAccel was investigated after the adjustment of age and ethnicity. No genetic variants passed the typical threshold for genome-wide significance (P < 5.00E-08). The quantile–quantile (Q-Q) plot are illustrated in Fig. S2. The genomic inflation factor of 1.02 indicated that the study exhibited very minor inflation and slight deviations in test statistics from the null distribution. A Manhattan plot was illustrated in Fig. 3C, highlighting the top 3 mapped genes (ZDHHC19, SIRPA, and PMEPA1). Boxplots were illustrated for the representative SNPs of the top 3 mapped genes (Additional file 1: Fig. S3A). The LocusZoom plots of ZDHHC19-rs9864994 and SIRPA-rs112608975 showed these two genes have multiple SNPs in LD (Additional file 1: Fig. S3B). The GWAS results of genetic variants with P < 1.00E-03 were shown in Additional file 3: Table S4A and a list of genetic variants with synonymous or missense mutations were illustrated in Additional file 3: Table S4B. Interestingly, missense mutations of NADPH oxidase 4 (NOX4), interleukin 4 receptor (IL4R), defensins (DEFB128 & DEFB127) and acyl-CoA synthetase bubblegum family member 2 (ACSBG2) were associated with PhenoAgeAccel.

For enrichment analysis, top 150 mapped genes (Additional file 3: Table S4C) from the GWAS results were analysed by Metascape (https//metascape.org) based on the following ontology sources: KEGG Pathway, GO Biological Processes, Reactome Gene Sets, Canonical Pathways, CORUM, WikiPathways, and PANTHER Pathway. Overall, top 20 clusters were found with their enriched terms with a p-value < 0.01, a minimum count of 3, and an enrichment factor > 1.5 (Additional file 3: Table S4D). Their associated pathways included calcium ion transmembrane transport, ERBB2 activates PTK signaling, regulation of cardiac muscle cell contraction, apoptotic process involved in development, circadian entrainment, memory, regulation of phosphatase activity and Ras signaling pathway etc. (Additional file 1: Fig. S4).

In our study, we also performed a candidate analysis of the SNPs reported in the GWAS results of PhenoAgeAccel from the UK biobank [18] and Taiwan biobank [15]. The results were shown in Additional file 3: Table S4E-F. Out of the 29 significant SNPs identified in the UK biobank, 26 SNPs were found in our SNP list after QC, with only two of them (IL6R-rs4129267: P = 9.87E-03 and FADS1/2-rs174548: P = 2.85E-02) showing weak associations (Additional file 3: Table S4E). Among the 11 significant SNPs identified in the Taiwan biobank, 8 SNPs remained in our SNP list after QC, with only one SNP (AXIN1-rs7206286: P = 1.34E-02) exhibiting a weak association (Additional file 3: Table S4F).

Gut microbiome association with PhenoAgeAccel

To explore the relationship between gut microbiome and PhenoAgeAccel, the alpha- and beta-diversity analysis were performed. PhenoAgeAccel was significantly inversely associated with 2 gut microbiome alpha-diversity measurements, Pielou’s evenness and Faith's phylogenetic diversity (Additional file 1: Table S5). However, the significance was not retained after accounting for confounding factors, including BMI, age, ethnicity, educational attainment, and parity (Additional file 1: Table S5). Principal Coordinate Analysis (PCoA) based on Unweighted UniFrac distance, a metric measuring gut microbial community dissimilarity, demonstrated a statistically significant association with PhenoAgeAccel. Notably, this relationship remained significant even after adjusting for the aforementioned confounding variables (Padj = 0.003, Fig. 4A and Additional file 1: Table S6).

Fig. 4

Association between the gut microbiome and PhenoAgeAccel. A Principal Coordinate Analysis of Unweighted UniFrac distance illustrating the gut microbiome of women with different PhenoAgeAccel. Padj was obtained using multi-way ADONIS permutation-based statistical test after adjusting for the effects of ethnicity, BMI, age, educational attainment and parity. B The feature importance of the top 14 gut microbial species identified through nested cross-validated random forest regression. C Three microbial species significantly associated with PhenoAgeAccel identified via MaAsLin2 analysis

To identify specific gut microbial species associated with PhenoAgeAccel, we employed a machine learning approach—nested cross-validated random forest regressor. This analysis identified the top 14 microbial species associated with PhenoAgeAccel, including Streptococcus salivarius, Akkermansia, Erysipelotrichaceae UCG-00, Dorea, Enterococcus, Megasphaera, Dorea, Blautia, Lachnoclostridium, Weissella, Allisonella, Massiliomicrobiota timonensis, Bifidobacterium, and Bacteroides vulgatus (Fig. 4B). Out of these, three microbial species retained their significant associations with PhenoAgeAccel after comprehensive adjustment for BMI, age, ethnicity, educational attainment, and parity (Fig. 4C and Additional file 1: Table S7). Erysipelotrichaceae UCG-003 and Bacteroides vulgatus were significantly negatively associated with PhenoAgeAccel, whereas Bifidobacterium showed an inverse association (Fig. 4C).

Network analysis of aging-related factors

A schematic diagram was generated to illustrate the factors linked to accelerated biological aging based on association results from different platforms (Fig. 5A). Subsequently, 73 aging-related factors were selected for network analysis. These include 4 factors identified in clinical measurements (Fig. 2G), and 36 blood biomarkers (Fig. 2G), 16 lipids (Padj < 0.05, Fig. 3B), 3 representative SNPs of the top 3 mapped genes (ZDHHC19, SIRPA and PMEPA1, Fig. S3A), and 14 gut microbial species (Fig. 4B). Firstly, to understand how these aging-related factors correlate with elements of PhenoAge (9 clinical biomarkers and CA), a network was generated based on their pair-wise correlation coefficients with |R|≥ 0.30 (Additional file 1: Fig. S5). From this network, ln(CRP) was the element most strongly correlated with adiposity measures, glycaemic traits, FAs, lipid species, kynurenine pathway metabolites, tyrosine, IGF-1, and GlycA, followed by white blood cell count and alkaline phosphatase, which also showing strong associations with these factors, except for lipid species, kynurenine pathway metabolites, tyrosine, and IGF-1. Fasting glucose was only strongly associated with glycaemic traits and adiposity measures. Albumin and creatinine were associated with fat mass and IGFBP-6, respectively.

Fig. 5

Network visualization of accelerated biological aging and aging-related factors. A Schematic diagram summarizing the factors linked to accelerated biological aging. B Network visualization of aging-related factors using Cytoscape. Each connection has a Spearman’s rank correlation coefficient of ≥ 0.30. Red – positive correlation and blue – negative correlation. Line width – magnitude of coefficient. These factors are grouped by their properties, denoted as different shapes of nodes

Next, to explore the roles of aging-related factors derived from different platforms in the biological aging process, the interconnections among these factors were investigated. The pairwise correlation heat map showed the overall connections between these factors with a p-value < 0.05 (Additional file 1: Fig. S6). From the heatmap, adiposity measures, blood biomarkers and plasma lipid species showed a very strong interconnection among themselves but had a relatively weaker association with SNPs or gut microbial species. Of the 3 SNPs, ZDHHC19-rs9864994 was inversely associated with IGF-1, IGF-2 and histidine, but positively associated with Cer(d18:1/18:0) and Lachnoclostridium. SIRPA-rs112608975 showed a positive association with thiamine, quinolinic acid and phenylalanine. PMEPA1-rs157092 exhibited a positive association with fat mass, liver fat, kynurenine pathway metabolites and 8 lipids from dihydroceramide, lysoalkenylphosphatidylethanolamine, lysophosphatidylinositol, alkylphosphatidylcholine, phosphatidylinositol and sphingosine while it showed an inverse association with HEI score, all-trans retinol, plasma PUFAs, DHA, and degree of unsaturation. Among the three significant gut microbial species, Erysipelotrichaceae UCG-003, which showed an inverse association with PhenoAgeAccel, also exhibited a negative association with fat mass, liver fat, choline, SFAs and 6 lipids from dihydroceramide, alkylphosphatidylcholine, phosphatidylinositol and sphingosine. But it was positively associated with pyridoxal phosphate, PUFAs and 2 lipids from alkylphosphatidylethanolamine. Conversely, Bifidobacterium, which showed a positive association with PhenoAgeAccel, showed an inverse association with the HEI score, IGF-2, all-trans retinol and 3 amino acids (tryptophan, histidine, and leucine). Additionally, it was positively associated with homocysteine, neopterin, SFAs and 3 lipids from ceramide, lysoalkenylphosphatidylethanolamine and alkylphosphatidylcholine.

To simplify the complicated interconnections among these factors (Additional file 1: Fig. S6), only factors with |R|≥ 0.30 were selected for network visualization (Fig. 5B). The 3 SNPs and 10 gut microbial species were not presented in the network due to their weak associations with other factors (|R|< 0.30). The left 4 gut microbial species were kept only due to their strong interconnections among themselves. This integrative network highlighted complex correlations within and between categories of adiposity measures, blood biomarkers, and plasma lipid species. Several intriguing findings were observed. First, we observed strong correlations among adiposity measures, glycaemic traits, lipid-related components (such as fatty acids, ApoB, and lipids), and GlycA. Most of these correlations were positive, except for PUFAs, DHA, and degree of unsaturation, which were inversely associated with GlycA, lipids, adiposity and glycaemia. Second, IGFBP-1 exhibited a robust inverse relationship with adiposity measures, insulin, and HOMA-IR. Third, amino acids including leucine, tyrosine and phenylalanine displayed pronounced positive associations with adiposity measures, glycaemic traits, kynurenine pathway metabolites, and glycoprotein acetyls. Fourth, kynurenine pathway metabolites showed positive associations with glycaemic traits, adiposity measures, and GlycA. Fifth, nicotinamide (Vitamin B3) exhibited a strong positive connection with plasma lipids from sphingosine, ceramide, dihydroceramide, alkylphosphatidylcholine and lysoalkenylphosphatidylethanolamine. Lastly, homocysteine was inversely associated with HEI score.

Mediation effects of blood biomarkers

The influences of diet, adiposity, genetic variants, and gut microbial species on PhenoAgeAccel may be contributed through clinical biomarkers and circulating metabolites. Mediation analyses were performed for 10 predictors (HEI score, fat mass, liver fat, visceral adipose tissue, ZDHHC19-rs9864994, SIRPA-rs112608975, PMEPA1-rs157092, Erysipelotrichaceae UCG-003, Bifidobacterium, and Bacteroides vulgatus) and 36 mediators (aging-related blood biomarkers). Aging-related plasma lipid species were excluded due to their concurrent measurement with blood biomarkers, precluding causal inference. Additionally, plasma FA measures and ApoB were used as lipid biomarkers in this analysis. A total of 62 linkages (Fig. 6, Additional file 4: Table S8) were identified to mediate the associations between these predictors and PhenoAgeAccel through blood biomarkers (PACME < 0.05), with 39 linkages remaining significant after multiple testing correction (Fig. 6, FDR < 0.2).

Fig. 6

Effects of mediators (blue squares) on the associations between predictors (green circles) and the outcome (PhenoAgeAccel). A Diet (heathy eating index score). B Adiposity (Fat mass, liver fat and visceral adipose tissue). C Gut microbial species (Erysipelotrichaceae UCG-003, Bacteroides vulgatus, and Bifidobacterium). D Genetic variants (ZDHHC19-rs9864994, SIRPA-rs112608975, and PMEPA1-rs157092). Age, ethnicity, educational attainment, parity and BMI were adjusted in analysis models. Each connection has a p-value of < 0.05 for average causal mediation effect (ACME), with a thicker line indicating an FDR of < 0.2

HEI score was negatively associated with PhenoAgeAccel, mediated by multiple biomarkers related to lipid metabolism (DHA, SFAs, and degree of unsaturation), insulin/IGF signalling (insulin and IGF-1/2), immune activation and inflammation (tryptophan, neopterin, and 3-hydroxykynurenine), and nutritional metabolism (betaine, retinol, pyridoxal phosphate, thiamine, and riboflavin). The positive associations between adiposity measures (fat mass, liver fat, and visceral adipose tissue) and PhenoAgeAccel were mediated by multiple biomarkers linked to glucose metabolism (2-h post-load glucose, HbA1c, and HOMA-IR), insulin/IGF signalling (insulin, IGF-1 and IGFBP-6), immune activation and inflammation (kynurenine pathway metabolites and GlycA), nutritional metabolism (retinol and leucine), and lipid metabolism (LA, SFAs, and ApoB). For genetic variants, the association between SIRPA-rs112608975 and PhenoAgeAccel was mediated by biomarkers of immune activation and inflammation (neopterin and kynurenine) and insulin resistance (insulin and HOMA-IR), while the association between ZDHHC19-rs9864994 and PhenoAgeAccel was through IGF signalling (IGF2 and IGFBP-1). For PMEPA1-rs157092, neopterin and quinolinic acid were potential mediators. Among gut microbial species, neopterin strongly mediated the association between Bifidobacterium and PhenoAgeAccel. Additionally, multiple kynurenine pathway metabolites were identified as potential mediators for Bacteroides vulgatus, while IGF-1/IGFBP-6, LA, phenylalanine and nicotinamide were potential mediators for Erysipelotrichaceae UCG-003.

Furthermore, from the mediation results of 3 gut microbial species in the associations between 7 predictors (diet, adiposity, and genetic variants) and PhenoAgeAccel, only one significant linkage was identified with PACME < 0.05. Erysipelotrichaceae UCG-003 was found to mediate the association between fat mass and PhenoAgeAccel (Fig. 6B, Additional file 4: Table S8).