Background A substantial body of evidence links frailty to a wide range of chronic diseases (CCD); however, the direction and strength of this causal relationship remain insufficiently understood. In this study, we systematically assess the relationship between the frailty index (FI) and CCD by integrating large-scale observational analyses with Mendelian randomization (MR) to provide more robust causal inference. Methods This national cross-sectional study used data from the first wave of the China health and retirement longitudinal study conducted in 2011. Multivariable logistic regression models were applied to examine the association between FI and CCD. Restricted cubic splines were used to characterize the dose–response relationship. In addition, a two-sample MR analysis was performed to explore the potential causal effect of FI on CCD, with the inverse-variance weighted (IVW) method serving as the primary analytical approach. Tests for heterogeneity and multiple sensitivity analyses were also carried out to ensure the robustness of the findings. Results In our analysis, the FI remained independently associated with an increased risk of multiple CCDs after adjustment for relevant covariates. Specifically, higher FI values were linked to elevated odds of hypertension (OR = 1.055, 95% CI 1.048–1.062), dyslipidemia (OR = 1.039, 95% CI 1.030–1.048), diabetes (OR = 1.055, 95% CI 1.045–1.065), chronic lung disease (OR = 1.061, 95% CI 1.052–1.070), asthma (OR = 1.056, 95% CI 1.045–1.067), cardiovascular disease (CVD; OR = 1.061, 95% CI 1.052–1.070), chronic kidney disease (CKD; OR = 1.049, 95% CI 1.039–1.059), arthritis (OR = 1.116, 95% CI 1.107–1.125), and stroke (OR = 1.091, 95% CI 1.077–1.105). In addition, FI demonstrated a significant non-linear association with all CCD outcomes (all p < 0.05). MR using the IVW method supported a causal effect of genetically predicted FI on several CCDs, including hypertension (OR = 1.779, 95% CI 1.400–2.261, p = 2.43 × 10–6), diabetes (OR = 1.781, 95% CI 1.382–2.295, p = 8.24 × 10–6), COPD (OR = 2.077, 95% CI 1.254–3.441, p = 4.55 × 10–3), CKD (OR = 1.647, 95% CI 1.118–2.426, p = 1.16 × 10–2), and stroke (OR = 1.561, 95% CI 1.218–2.001, p = 4.32 × 10–4). However, no significant causal effect was observed for hyperlipidemia (OR = 1.366, 95% CI 0.467–3.996, p = 0.569), CVD (OR = 1.359, 95% CI 0.994–1.858, p = 0.054), asthma (OR = 1.244, 95% CI 0.759–2.037, p = 0.386), or arthritis (OR = 1.688, 95% CI 0.680–4.192, p = 0.259). Conclusions This study demonstrates that frailty is broadly associated with chronic diseases affecting multiple physiological systems. Moreover, the FI exhibited a causal effect on hypertension, type 2 diabetes, COPD, stroke, and CKD, whereas no causal associations were identified for hyperlipidemia, asthma, CVD, or arthritis. Taken together, these results provide new insights into the complex interplay between frailty and chronic disease development.

Association of frailty index with chronic diseases: from China Health and Retirement Longitudinal Study and a two-sample Mendelian randomization

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2026年3月11日

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Lei Yang,
TingTing Zeng,
Hongmei Yue

摘要

Background

A substantial body of evidence links frailty to a wide range of chronic diseases (CCD); however, the direction and strength of this causal relationship remain insufficiently understood. In this study, we systematically assess the relationship between the frailty index (FI) and CCD by integrating large-scale observational analyses with Mendelian randomization (MR) to provide more robust causal inference.

Methods

This national cross-sectional study used data from the first wave of the China health and retirement longitudinal study conducted in 2011. Multivariable logistic regression models were applied to examine the association between FI and CCD. Restricted cubic splines were used to characterize the dose–response relationship. In addition, a two-sample MR analysis was performed to explore the potential causal effect of FI on CCD, with the inverse-variance weighted (IVW) method serving as the primary analytical approach. Tests for heterogeneity and multiple sensitivity analyses were also carried out to ensure the robustness of the findings.

Results

In our analysis, the FI remained independently associated with an increased risk of multiple CCDs after adjustment for relevant covariates. Specifically, higher FI values were linked to elevated odds of hypertension (OR = 1.055, 95% CI 1.048–1.062), dyslipidemia (OR = 1.039, 95% CI 1.030–1.048), diabetes (OR = 1.055, 95% CI 1.045–1.065), chronic lung disease (OR = 1.061, 95% CI 1.052–1.070), asthma (OR = 1.056, 95% CI 1.045–1.067), cardiovascular disease (CVD; OR = 1.061, 95% CI 1.052–1.070), chronic kidney disease (CKD; OR = 1.049, 95% CI 1.039–1.059), arthritis (OR = 1.116, 95% CI 1.107–1.125), and stroke (OR = 1.091, 95% CI 1.077–1.105). In addition, FI demonstrated a significant non-linear association with all CCD outcomes (all p < 0.05). MR using the IVW method supported a causal effect of genetically predicted FI on several CCDs, including hypertension (OR = 1.779, 95% CI 1.400–2.261, p = 2.43 × 10^–6), diabetes (OR = 1.781, 95% CI 1.382–2.295, p = 8.24 × 10^–6), COPD (OR = 2.077, 95% CI 1.254–3.441, p = 4.55 × 10^–3), CKD (OR = 1.647, 95% CI 1.118–2.426, p = 1.16 × 10^–2), and stroke (OR = 1.561, 95% CI 1.218–2.001, p = 4.32 × 10^–4). However, no significant causal effect was observed for hyperlipidemia (OR = 1.366, 95% CI 0.467–3.996, p = 0.569), CVD (OR = 1.359, 95% CI 0.994–1.858, p = 0.054), asthma (OR = 1.244, 95% CI 0.759–2.037, p = 0.386), or arthritis (OR = 1.688, 95% CI 0.680–4.192, p = 0.259).

Conclusions

This study demonstrates that frailty is broadly associated with chronic diseases affecting multiple physiological systems. Moreover, the FI exhibited a causal effect on hypertension, type 2 diabetes, COPD, stroke, and CKD, whereas no causal associations were identified for hyperlipidemia, asthma, CVD, or arthritis. Taken together, these results provide new insights into the complex interplay between frailty and chronic disease development.