Genetically proxied glucagon-like peptide-1 and glucose-dependent insulinotropic polypeptide receptor pathway modulation is associated with favorable aging phenotypes: a drug-target Mendelian randomization study

Abstract

Background

Glucagon-like peptide-1 receptor (GLP1R) agonists and dual glucose-dependent insulinotropic polypeptide receptor/glucagon-like peptide-1 receptor (GIPR/GLP1R) agonists are established treatments for diabetes and obesity, but their potential effects on biological aging remain uncertain.

Methods

In this drug target Mendelian randomization study, we assessed the associations of genetically proxied modulation of GLP1R, GIPR, and dual GIPR/GLP1R pathways with four aging phenotypes, including frailty index, phenotypic age (PhenoAge) acceleration, telomere length, and longevity.

Results

Glycated hemoglobin (HbA1c) lowering via GIPR, modeling the glucose-lowering effect of variants in GIPR loci, was consistently associated with a lower frailty index (primary dataset: β = − 0.10 [95% CI: − 0.18, − 0.02]; replication dataset: − 0.14 [− 0.27, − 0.01]), reduced PhenoAge acceleration (primary: − 0.82 [− 1.17, − 0.48]; replication − 1.04 [− 1.81, − 0.27]), longer telomere length (primary: 0.06 [0.003, 0.11]; replication: 0.09 [0.002, 0.19]), and greater odds of longevity (primary: odds ratio [OR] = 2.90 [95% CI: 1.93, 4.36]; replication: 6.54 [2.92, 14.65]). Body mass index (BMI) lowering via GIPR, modeling appetite-suppressing and weight-reducing effects of variants, was associated with a lower frailty index (primary: − 0.07 [− 0.13, − 0.01]; replication: − 0.19 [− 0.35, − 0.04]) and higher odds of longevity (primary: 2.11 [1.22, 3.66]). In contrast, HbA1c lowering via GLP1R was associated exclusively with longevity (primary: OR = 3.72 [2.25, 6.18]; replication: 16.53 [4.28, 63.80]), whereas BMI lowering via GLP1R was not associated with any aging phenotype. Analyses of dual GIPR/GLP1R yielded results broadly similar to those for GIPR, with the exception of a null effect on frailty index.

Conclusions

Genetically proxied HbA1c- and BMI-lowering effects through GLP1R- and GIPR-related pathways were favorably associated with aging phenotypes, with broader associations observed for HbA1c lowering. Further clinical studies are warranted to clarify the relevance of these pathways to healthy aging.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-026-03304-y.

Research insights

What is currently known about this topic?

GLP1R and dual GIPR/GLP1R agonists provide established cardiovascular and renal benefits.

However, their effects on biological aging remain unclear.

What is the key research question?

Are GLP1R- and GIPR-related HbA1c- or BMI-lowering pathways associated with biological aging?

What is new?

• GIPR modulation was favorably associated with all four aging phenotypes.

• Dual GIPR/GLP1R modulation was favorable for aging phenotypes except frailty.

• HbA1c lowering showed broader aging associations than BMI lowering.

How might this study influence clincal practice?

These pathways warrant further clinical evaluation incorporating multidimensional aging outcomes.

Introduction

Aging is a multifaceted process characterized by various biological changes over time, such as the gradual accumulation of molecular and cellular damage, which can lead to functional decline, increased vulnerability to chronic diseases, and ultimately mortality [1, 2]. Within the geroscience framework, aging is increasingly recognized as a major modifiable driver of age-related diseases, implying that interventions targeting biological aging may prevent or delay multiple conditions simultaneously, thereby enhancing health, function, and independence in late‐life [3].

The remarkable clinical success of glucagon-like peptide-1 receptor (GLP1R) agonists in the treatment of type 2 diabetes (T2D) and obesity has stimulated intense interest in their pleiotropic effects beyond glycaemic control and weight loss [4]. Accumulating evidence suggests that these agents confer benefits across a broad range of age-related conditions, including cardiovascular, renal, hepatic, neurodegenerative, musculoskeletal diseases, and cancer in humans [5, 6], underscoring the geroprotective potential of GLP1R agonists [7]. Given the central role of biological aging in the development of age-related disorders including cardiovascular diseases, clarifying these aging-related effects has important clinical and public health implications [8, 9]. However, direct evidence for their associations with biological aging remains scarce, as clinical studies have yet to assess aging as a primary endpoint. Notably, recent preclinical data showed that low-dose exenatide broadly counteracted aging-associated alterations at the transcriptomic, methylomic, and metabolomic levels across multiple murine tissues and circulating leukocytes, positioning GLP1R agonism as a promising anti-aging therapy [10]. In parallel, the glucose-dependent insulinotropic polypeptide receptor (GIPR) has emerged as an important pharmacological target. Beyond its established role in glucose metabolism, GIPR signaling has also been implicated in the regulation of adipose tissue metabolism, insulin sensitivity, and energy balance [11]. In clinical trials, tirzepatide, a dual GIPR/GLP1R agonist, has shown greater reductions in plasma glucose and body weight than GLP1R agonist alone [12, 13]. However, whether such superior metabolic effects could translate into broader benefits for biological aging is unclear.

Given the novelty of GLP1R and GIPR as drug targets and the lack of randomized clinical trials with biological aging outcomes, drug-target Mendelian randomization (MR) provides an important approach for early evidence generation. By leveraging genetic variants associated with drug targets as instrumental variables, drug-target MR can simulate the physiological effects of pharmacological interventions and prioritize the potential therapeutic pathways for clinical translation before extensive trial data become available [14–16]. In this study, we conducted biomarker MR within a two-sample MR framework to examine the effects of glycated hemoglobin (HbA1c) and body mass index (BMI) on biological aging, and subsequently applied drug-target MR to evaluate the associations of genetically proxied modulation of GLP1R, GIPR, and dual GIPR/GLP1R with biological aging.

Methods

Study design

Figure 1 depicts the overall study framework. First, we identify single-nucleotide polymorphisms (SNPs) within or near GIPR or GLP1R loci to construct genetic instruments for GLP1R and GIPR, proxying the expected physiological responses to pharmacological target modulation including lower HbA1c and reduced BMI. Then, we performed biomarker MR analyses within a two-sample MR framework to evaluate whether HbA1c and BMI are causally associated with biological aging, thereby providing biological justification for subsequent drug-target MR analyses. Finally, we applied drug-target MR to assess the associations of genetically proxied modulation of the GLP1R and GIPR pathways with aging phenotypes. Three fundamental assumptions were behind the MR approach: (1) genetic variants and exposure are strongly correlated (relevance); (2) genetic variants are independent of the confounders influencing the relationship between exposure and outcome (independence); (3) genetic variants affect the outcome only through the exposure of interest (exclusion restriction) [14]. This study followed the MR Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [17]. Data sources were based on publicly available summary-level data from genome-wide association studies (GWAS) (Tables 1 and S1).

Fig. 1.

Overview of the study design. BMI body mass index, Chr chromosome; GIPR glucose-dependent insulinotropic polypeptide receptor, GLP1R Glucagon-like peptide-1 receptor; HbA1c glycated hemoglobin; PhenoAge phenotypic age; SNP single-nucleotide polymorphism; T2D type 2 diabetes

Table 1.

Overview of the GWAS data used in the study

Category	Consortium/Project	Trait assessed	Units	Sample size (case/control)	Ancestry	Year of publication or release	PubMed identifier
GLP1R and GIPR instrumentation
Primary instruments	Million Veterans Program (MVP)	HbA1c	SD	338,848	European	2024	39024449
		BMI	SD	424,221	European	2024	39024449
Data for replication	FinnGen R12	HbA1c	SD	332,429	European	2024	36653562
	GIANT	BMI	SD	681,275	European	2018	30124842
Positive controls
	DIAGRAM	T2D	log odds	242,283/1812,013	European	2024	38374256
	FinnGen R12	Obesity	log odds	31,499/500,192	European	2024	36653562
Aging phenotypes
	Meta	Frailty index	SD	175,226	European	2021	34431594
	UK Biobank	PhenoAge acceleration	Year	98,446	European	2021	34038024
	UK Biobank	Telomere length	Score	438,351	European	2024	39192095
	Meta	Longevity	log odds	11,262/25,483	European	2019	31413261

BMI body mass index, GIPR glucose-dependent insulinotropic polypeptide receptor, GLP1R Glucagon-like peptide-1 receptor, HbA1c glycated hemoglobin, SD standard deviation, PhenoAge phenotypic age, T2D type 2 diabetes

Proxies for GLP1R and GIPR modulation

Using GWAS summary statistics from the Million Veterans Program (MVP) [18], we identified independent genetic variants associated with HbA1c and BMI at genome-wide significance (P < 5 × 10⁻⁸), with linkage disequilibrium (LD) clumping at R² < 0.001 within 10,000 kb. For replication analyses, we further used GWAS summary statistics from FinnGen R12 [19] and the GIANT consortium [20] to identify independent variants significantly associated with HbA1c and BMI, respectively.

To proxy the effects of GIPR and GLP1R analogs, we developed genetic instruments for GIPR and GLP1R based on HbA1c and BMI, as these traits capture the core biological and clinical effects of their agonists. HbA1c reflects glucose-lowering effect, as both GIP and GLP-1 improve glycemic control by stimulating insulin secretion and suppressing glucagon release. In clinical practice, GLP1R agonists (e.g., semaglutide) and dual GIPR/GLP1R agonists (e.g., tirzepatide) markedly reduce HbA1c levels [21], making it an appropriate proxy for their metabolic effects. Likewise, BMI serves as an indicator for appetite suppression and weight reduction, which are two hallmark therapeutic features of GLP1R agonists and dual agonists [22]. Activation of GLP1R reduces food intake through central appetite regulation and delayed gastric emptying, whereas co-activation of GIPR may further enhance fat metabolism and energy expenditure, producing synergistic effects on weight loss [6]. Given that these agents are primarily used for the treatment of diabetes and obesity, instrumenting GIPR and GLP1R using HbA1c and BMI in MR enables us to approximate their long-term physiological effects in real-world scenarios.

Genetic instrument construction

We adopted a cis-instrument strategy to construct genetic instruments for GIPR and GLP1R by selecting genetic variants within each drug target locus [15]. For GLP1R, we selected independent SNPs (LD R² < 0.001) associated with HbA1c at genome-wide significance (P < 5 × 10⁻⁸) in MVP participants and located within ± 500 kb of the GLP1R locus (chromosome 6: 39,048,781-39,091,303 on GRCh38/hg38). For GIPR, the same criteria were applied to select independent SNPs within ± 500 kb of the GIPR locus (chromosome 19:45,668,221-45,683,722) that were significantly associated with HbA1c. In addition, GLP1R and GIPR instruments were also separately constructed based on BMI GWAS data from MVP participants. To proxy dual GIPR/GLP1R [23], variants from both loci were combined into joint instruments for glucose-lowering and BMI-related effects. To further assess the robustness of the findings, we performed replication analyses using HbA1c GWAS data from FinnGen R12 [19] and BMI GWAS data from the GIANT consortium [20], respectively, and reconstructed the genetic instruments for GLP1R, GIPR, and dual GIPR/GLP1R pathways. Since no BMI-associated variant within the GLP1R locus reached genome-wide significance, we applied a more relaxed significance threshold (P < 5 × 10⁻⁶), a strategy commonly used in cis-instrument MR studies when no conventional genome-wide significant variant is available [24, 25].

The instruments were validated using T2D and obesity as positive-control outcomes to ensure they captured the metabolic effects of genetically proxied GLP1R, GIPR, and dual GIPR/GLP1R pathways [23, 26, 27]. HbA1c-based instruments were primarily assessed against T2D risk, whereas BMI-based instruments were assessed against obesity risk. Cross-trait associations were also examined to fully characterize metabolic consequences. GWAS data for T2D were obtained from the DIAGRAM consortium, including 242,283 cases and 1,812,013 controls of European ancestry [28], and obesity data were derived from the FinnGen R12 [19].

Outcome

Given the absence of a universally accepted gold-standard biomarker of aging, we included the frailty index [29], phenotypic age (PhenoAge) acceleration [30], telomere length [31], and longevity [32] as complementary measures to capture aging in a multidimensional manner, in line with previous studies. GWAS data for frailty index were derived from the most recent GWAS meta-analysis (n = 175,226) [33], whereas data for PhenoAge acceleration (n = 98,446) and telomere length (n = 438,351) were obtained from the UK Biobank [34, 35]. Longevity GWAS data were drawn from a meta-analysis of European-ancestry populations, in which cases were individuals who survived beyond the 90th percentile of age (n = 11,262) and controls were those whose age at death or last follow-up was at or below the 60th percentile (n = 25,483) [32]. No sample overlap was identified between the primary exposure and outcome datasets. Further details of the GWAS data sources for each outcome are provided in Table S1.

Statistical analysis

When an instrumental SNP was unavailable in an outcome GWAS dataset, a proxy SNP in high LD (r² ≥ 0.8) was used, where available. The inverse-variance weighted (IVW) method was used for the primary MR analyses, with weighted median, maximum likelihood, and MR-Egger regression as complementary methods. Cochran’s Q test and the MR-Egger intercept were used to assess heterogeneity and horizontal pleiotropy, respectively. Instrument strength was evaluated using the F statistic, with F statistic < 10 indicating potential weak-instrument bias. MR Steiger directionality tests were applied to evaluate reverse causality, and leave-one-out analyses were performed to verify whether the removal of a single influential SNP influenced the overall estimates.

In biomarker MR analyses, effect estimates correspond to the outcome change per 1-standard deviation (SD) increase in genetically predicted HbA1c or BMI. In drug-target MR analyses, estimates represent the change associated with a 1-SD decrease in genetically predicted decrease in HbA1c or BMI mediated through modulation of the corresponding drug target.

All statistical analyses were performed in R (version 4.4.1) using the “TwoSampleMR” package. All tests were two-sided, with P < 0.05 considered statistically significant. To account for multiple hypothesis testing across the four aging phenotypes, false discovery rate (FDR)-adjusted P values were calculated using the Benjamini–Hochberg method.

Results

Effects of HbA1c and BMI on aging phenotypes

We identified 567 and 822 independent SNPs associated with HbA1c and BMI, respectively, in the primary datasets, and 276 and 535 corresponding SNPs in the replication datasets, as instrumental variables (Table S2). Genetically predicted higher HbA1c levels were associated with increased frailty index (primary: β [95% CI] = 0.04 [0.03, 0.06]; replication: 0.06 [0.04, 0.08]), greater PhenoAge acceleration (primary: 0.50 [0.41, 0.59]; replication: 0.59 [0.39, 0.80]), shorter telomere length (primary: − 0.01 [− 0.02, − 0.002]; replication: − 0.01 [− 0.03, 0.01]), and lower odds of longevity (primary: OR [95% CI] = 0.85 [0.79, 0.91]; replication: 0.83 [0.73, 0.95]) (Tables 2 and S3). Similar associations were observed for genetically predicted higher BMI levels in both the primary and replication analyses.

Table 2.

Associations of genetically predicted HbA1c and BMI levels with aging phenotypes

Biomarker	Frailty index		PhenoAge acceleration		Telomere length		Longevity
	Beta (95% CI)	P value	Beta (95% CI)	P value	Beta (95% CI)	P value	OR (95% CI)	P value
HbA1c
Primary	0.04 (0.03, 0.06)	4.49E−10	0.50 (0.41, 0.59)	1.31E−25	− 0.01 (− 0.02, − 0.002)	1.50E−02	0.85 (0.79, 0.91)	1.08E−06
Replication	0.06 (0.04, 0.08)	9.00E−07	0.59 (0.39, 0.80)	1.63E−08	− 0.01 (− 0.03, 0.01)	2.40E−01	0.83 (0.73, 0.95)	5.49E−03
BMI
Primary	0.05 (0.04, 0.07)	1.55E−18	0.43 (0.36, 0.50)	1.34E−33	− 0.01 (− 0.02, − 0.01)	8.39E−04	0.89 (0.84, 0.94)	1.10E−04
Replication	0.22 (0.19, 0.25)	3.79E−55	1.27 (1.11, 1.43)	1.47E−55	− 0.06 (− 0.07, − 0.04)	3.47E−12	0.73 (0.65, 0.83)	9.95E−07

BMI body mass index, CI confidence interval, HbA1c glycated hemoglobin, OR odds ratio, PhenoAge phenotypic age

Effects of GIPR and GLP1R analogs on T2D and obesity

Using MVP GWAS as the primary data, we identified 24 HbA1c-associated and 22 BMI-associated SNPs within the GIPR locus, and 18 HbA1c-associated and 18 BMI-associated SNPs within the GLP1R locus, as genetic instruments (Table S4). The F statistics for SNPs used to proxy GLP1R and GIPR activity ranged from 29.81 to 442.96, indicating that weak instrument bias was unlikely to affect the findings. In the replication data, we identified 12 and 6 HbA1c-associated SNPs within the GIPR and GLP1R loci, respectively, and 11 and 3 BMI-associated SNPs within the corresponding loci, with F statistics ranging from 20.97 to 259.06.

For GIPR, genetically proxied lower HbA1c via GIPR was robustly associated with a reduced T2D risk, with consistent effect estimates across the primary (OR [95% CI] = 0.55 [0.43, 0.70]) and replication data (0.35 [0.20, 0.63]) (Fig. 2 and Table S5). Similarly, lower BMI by GIPR was associated with a lower risk of obesity (primary: 0.59 [0.50, 0.68]; replication: 0.33 [0.18, 0.60]). For GLP1R, genetically proxied lower HbA1c mediated through GLP1R was consistently associated with a lower risk of T2D (primary: 0.59 [0.46, 0.74]; replication: 0.30 [0.14, 0.64]). Genetically proxied lower BMI through GLP1R was also associated with a lower risk of obesity (primary: 0.43 [0.35, 0.52]; replication: 0.20 [0.01, 3.60]). For combined GIPR/GLP1R, genetically proxied lower HbA1c through dual receptor activation was associated with a substantially lower risk of T2D (primary: 0.56 [0.49, 0.65]; replication: 0.33 [0.21, 0.52]), and genetically proxied lower BMI through dual receptor activation was associated with a lower risk of obesity (primary: 0.52 [0.46, 0.60]; replication: 0.31 [0.15, 0.61]). The consistent results across different datasets support the validity of the genetic instruments for GIPR and GLP1R.

Fig. 2.

Drug-target MR results of positive control analyses for GIPR and GLP1R instrument validation. Results are shown separately for genetically proxied reductions in HbA1c (left) and BMI levels (right), two primary mechanisms through which these agonists exert their clinical effects. Odds ratios (OR) and 95% confidence intervals (CI) are displayed, with MR estimates derived from biomarker (BMI or HbA1c) data using the primary and replication data sources. BMI body mass index; CI confidence interval; GIPR glucose-dependent insulinotropic polypeptide receptor; GLP1R Glucagon-like peptide-1 receptor; HbA1c glycated hemoglobin; MR Mendelian randomization; T2D type 2 diabetes

Effects of GIPR and GLP1R analogs on aging phenotypes

HbA1c lowering via GIPR (primary: β [95% CI] = − 0.10 [− 0.18, − 0.02]; replication: − 0.14 [− 0.27, − 0.01]), but not GLP1R or dual GIPR/GLP1R, was associated with a lower frailty index in both the primary and replication analyses (Fig. 3, Table S6). Similarly, BMI lowering via GIPR was consistently associated with a lower frailty index across both datasets (primary: − 0.07 [− 0.13, − 0.01]; replication: − 0.19 [− 0.35, − 0.04]), whereas no association was observed for GLP1R or dual GIPR/GLP1R. For PhenoAge acceleration, HbA1c lowering via dual GIPR/GLP1R was associated with decreased PhenoAge acceleration in the primary dataset (β [95% CI] = − 0.47 [− 0.75, − 0.20]). The protective association was observed for HbA1c lowering via GIPR, but not GLP1R, across the primary (− 0.82 [− 1.17, − 0.48]) and replication datasets (− 1.04 [− 1.81, − 0.27]). Consistently, HbA1c lowering via dual GIPR/GLP1R was associated with increased telomere length in the primary dataset (β [95% CI] = 0.04 [0.001, 0.07]), while HbA1c lowering via GIPR exhibited significant estimates in both the primary (0.06 [0.003, 0.11]) and replication datasets (0.09 [0.002, 0.19]), suggesting that GIPR modulation is responsible for the observed relationships. Notably, BMI lowering via GIPR, GLP1R, and dual GIPR/GLP1Rshowed no significant association with PhenoAge acceleration or telomere length.

Fig. 3.

Drug-target MR estimates of GIPR and GLP1R modulation on aging phenotypes. Results are displayed separately for genetically proxied reductions in HbA1c (left) and BMI levels (right), reflecting distinct physiological mechanisms of these agonists. Beta values or odds ratios (OR) and 95% confidence intervals (CI) are shown for both primary and replication datasets. BMI body mass index, CI confidence interval; GIPR glucose-dependent insulinotropic polypeptide receptor, GLP1R Glucagon-like peptide-1 receptor; HbA1c glycated hemoglobin; MR Mendelian randomization; PhenoAge, phenotypic age

Regarding longevity, HbA1c lowering via GIPR (primary: OR [95% CI] = 2.90 [1.93, 4.36]; replication: 6.54 [2.92, 14.65]), GLP1R (primary: 3.72 [2.25, 6.18]; replication: 16.53 [4.28, 63.80]), and dual GIPR/GLP1R (primary: 3.20 [2.33, 4.39]; replication: 8.34 [4.17, 16.67]) was consistently associated with increased odds of longevity in both the primary and replication analyses. Similarly, BMI lowering via dual GIPR/GLP1R was associated with increased odds of longevity in both datasets (primary: OR [95% CI] = 1.95 [1.32, 2.87]; replication: 4.13 [1.03, 16.51]), whereas BMI lowering via GIPR showed significant association with longevity only in the primary dataset (2.11 [1.22, 3.66]). No significant association was observed for BMI lowering via GLP1R.

After FDR correction, all nominally significant associations remained statistically significant except for BMI lowering via GIPR with frailty index in the replication dataset (P_FDR = 0.061), HbA1c lowering via dual GIPR/GLP1R with telomere length in the primary dataset (P_FDR = 0.055), and BMI lowering via dual GIPR/GLP1R with longevity in the replication dataset (P_FDR = 0.077) (Table S7). Sensitivity analyses using complementary MR methods produced effect estimates aligned in magnitude and direction with the primary results (Table S6). No substantial heterogeneity or strong evidence of horizontal pleiotropy was detected. Furthermore, the Steiger test indicated correct causal orientation (Table S6) and leave-one-out analysis revealed that the pooled effect estimates were not materially affected by the removal of any single SNP (Figs. S1 and S2).

Discussion

In this drug-target MR study, genetically proxied GIPR modulation showed the consistently favorable associations across multidimensional aging phenotypes, including a lower frailty index, reduced PhenoAge acceleration, longer telomere length, and greater odds of longevity. Dual GIPR/GLP1R was also associated with favorable aging phenotypes except for frailty index, whereas GLP1R was associated exclusively with longevity. Of note, HbA1c lowering via GIPR or GLP1R loci exhibited broader associations with healthy aging than BMI lowering. Overall, the pattern was heterogeneous, with the most consistent associations observed for GIPR-related HbA1c lowering, whereas findings for BMI-related pathways and GLP1R were more limited.

The divergence in the effects observed between GLP1R and GIPR instrumental analyses likely reflects the broader impact of GIPR-related pathways on systemic metabolic homeostasis through improvements in insulin sensitivity, lipid handling, and energy balance [11, 36, 37] compared with GLP1R. This heterogeneity may also be attributed to the fact that these aging phenotypes are indirect measures capturing distinct dimensions of aging. Unlike longevity, which reflects survival as an integrative endpoint, telomere length is commonly regarded as a marker of cellular aging [31], while PhenoAge captures systemic aging burden [8, 30] and frailty reflects the functional dimension of aging as a manifestation of reduced physiological reserve [38]. In this context, the broader metabolic actions of GIPR-related pathways may be more readily captured across cellular, systemic and functional aging domains. By contrast, although GLP1R agonists have established cardiometabolic and cardiorenal benefits [21, 39], their effects on intermediate aging-related phenotypes may be less straightforward. The findings for dual GIPR/GLP1R further suggest that combined receptor activation does not necessarily yield uniformly stronger associations across aging domains, and the relative contribution of each receptor may vary depending on the aging metrics examined. Importantly, effect estimates are not directly comparable across aging phenotypes because the outcomes were measured on different scales.

Notably, HbA1c lowering via GIPR or GLP1R variants showed broader associations with healthy aging than BMI lowering. In our study, HbA1c lowering via GIPR was consistently associated with all aging phenotypes, whereas BMI lowering via GIPR was associated only with frailty index and longevity. For GLP1R, the association with longevity was also observed for HbA1c lowering but not for BMI lowering effects. This pattern suggests that the aging-related signals captured via these loci are more strongly influenced by glycemic and metabolic effects than by weight reduction. From a mechanistic perspective, HbA1c lowering via GIPR or GLP1R may reflect improved glucose homeostasis, enhanced insulin action and downstream reductions in metabolic stress and inflammation. These processes closely map onto core hallmarks of aging, including deregulated nutrient sensing, mitochondrial dysfunction and chronic inflammation [40]. Furthermore, these mechanisms are also involved in cardiovascular aging, as persistent metabolic and inflammatory stress contributes to endothelial dysfunction, vascular senescence, and arterial stiffening. Nevertheless, BMI is a crude anthropometric measure that does not adequately capture adipose tissue function, ectopic fat deposition or tissue-specific metabolic stress. This may explain why BMI-lowering effects demonstrated less consistent associations across intermediate aging phenotypes [41, 42].

These findings may help prioritize incretin-related metabolic pathways for further investigation in aging research. To date, the only approved therapy targeting GIPR is the dual GIPR/GLP1R agonist (eg, tirzepatide) [43]. The favorable associations observed between dual modulation and several aging outcomes, together with the well-established metabolic and cardiorenal benefits of GLP1R agonists [39], support continued mechanistic and clinical evaluation of these pathways in the context of aging. Importantly, the more consistent associations observed for GIPR alone across diverse aging phenotypes, compared with dual GIPR/GLP1R, suggest potential value in the development of GIPR-targeted therapies, especially given that selective GIPR agonists have already entered early-phase clinical development [44]. However, in older adults, these potential benefits should be balanced against possible fat-free mass loss during weight reduction, which may exacerbate sarcopenia. Future trials should therefore directly assess changes in muscle mass and function, particularly for dual GIPR/GLP1R [45–47].

The strengths of this study included the use of large-scale genetic datasets, multiple aging phenotypes, and a two-stage MR design integrating biomarker MR with drug-target MR to strengthen causal inference. The consistency across independent datasets for each biomarker further reinforces the robustness of the results. However, several limitations should be acknowledged. First, MR estimates reflect lifelong genetically proxied perturbations and therefore may not directly recapitulate the magnitude, timing, duration of pharmacological treatment effects, or differences among specific drug classes and formulations. Moreover, combining variants across the GIPR and GLP1R loci provides only a simplified proxy for dual receptor modulation and may not fully reflect its pharmacological complexity. Second, HbA1c and BMI are downstream metabolic traits rather than direct measures of GLP1R or GIPR activity. Accordingly, our cis-instruments estimate receptor loci-associated HbA1c- and BMI-lowering effects, rather than direct receptor activation or the full pharmacological effects of agonists. Moreover, although restricting to the target loci and conducting positive-control analyses support instrument relevance, broader metabolic pathways and regulatory influences from nearby genes cannot be fully excluded. Our findings should therefore be interpreted as evidence for glycemic and adiposity pathways associated with genetic variation at the GLP1R and GIPR loci, whereas effects independent of glucose lowering and weight reduction remain uncaptured [6, 37]. Third, weak regional association signals for the outcomes at the GLP1R and GIPR loci limited the utility of colocalization analyses [48]. Thus, LD with nearby variants cannot be excluded, and locus-specific attribution should be interpreted cautiously. Fourth, several replication estimates, particularly for GLP1R-mediated BMI lowering, had wide confidence intervals due to the limited number of instruments and require validation in larger GWAS datasets. Fifth, although frailty index, PhenoAge acceleration, telomere length, and longevity represent complementary dimensions of aging, none of them alone can capture the complex aging process. Future studies integrating proteomics, metabolomics, and other multi-omics approaches are needed to comprehensively characterize the molecular landscape of aging and to further validate the effects of GLP1R- and GIPR-targeted therapies [49–51]. Last, since our analyses were restricted to individuals of European ancestry, the generalizability of these findings to other ethnic groups remains uncertain.

Conclusion

This study provides genetic evidence that HbA1c- and BMI-lowering effects through GLP1R- and GIPR-related pathways, particularly GIPR-related pathways, were favorably associated with several aging phenotypes. The broader associations observed for HbA1c lowering suggest a greater role for metabolic regulation than for appetite suppression and weight loss. Further clinical evaluation of GLP1R- and GIPR-targeted therapies is warranted to clarify their potential relevance to healthy aging.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

BMI

Body mass index

GIPR

Glucose-dependent insulinotropic polypeptide receptor

GLP1R

Glucagon-like peptide-1 receptor

GIPR/GLP1R

Dual glucose-dependent insulinotropic polypeptide receptor/glucagon-like peptide-1 receptor

GWAS

Genome-wide association study

HbA1c

Glycated hemoglobin

LD

Linkage disequilibrium

MR

Mendelian randomization

PhenoAge

Phenotypic age

SNP

Single-nucleotide polymorphism

T2D

Type 2 diabetes

Author contributions

B.W. and Y.L. conceived and designed the study. Jiang.L and Jie. L performed the statistical analysis and drafted the manuscript. X.X participated in data collection. N.W., and B.W., critically revised the manuscript. All authors reviewed the manuscript drafts, critically revised the manuscript, and approved the final manuscript. B.W. and Y.L. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Funding

National Natural Science Foundation of China (82404337, 82170870, and 82120108008).

Data availability

This study used publicly available data. References and data links are available in Table S1. The analytic code used will be made available from the corresponding author upon request.

Declarations

Ethics approval and consent to participate

Summary-level GWAS statistics used in this study are publicly available and no specific ethical approval was required.

Competing interests

The authors declare no competing interests.