Associations of preserved ratio impaired spirometry with cardiometabolic comorbidities: a systematic review and meta-analysis

Abstract

Background

Preserved ratio impaired spirometry (PRISm) is a prevalent lung function abnormality associated with an increased body mass index and an increased risk of cardiovascular disease and metabolic disorders. However, the strength and consistency of these associations across populations remain unclear. This systematic review and meta-analysis aimed to quantify the relationship between PRISm and key cardiometabolic comorbidities, including diabetes mellitus, hypertension, hypercholesterolaemia, ischaemic heart disease and heart failure.

Methods

A systematic search of PubMed, Embase and Web of Science was conducted to identify observational studies comparing the prevalence of cardiometabolic comorbidities in PRISm and normal spirometry populations. Meta-analyses were performed for conditions reported in three or more studies, and heterogeneity was assessed using the I² statistic. Sensitivity and influence analyses were conducted to ensure the robustness of findings.

Results

A total of 18 studies were included, comprising over 500 000 participants. Meta-analysis showed significant associations between PRISm and diabetes (OR 2.08, 95% CI 1.78–2.42), hypertension (OR 1.78, 95% CI 1.55–2.03), ischaemic heart disease (OR 2.05, 95% CI 1.59–2.64), heart failure (OR 2.82, 95% CI 1.40–5.67) and hypercholesterolaemia (OR 1.46, 95% CI 1.16–1.85). PRISm populations also exhibited a higher body mass index (mean difference 1.49 kg·m⁻², 95% CI 0.92–2.05 kg·m⁻²).

Conclusion

PRISm is strongly associated with cardiometabolic disease, reinforcing its role as a systemic condition rather than a purely pulmonary abnormality. These findings highlight the need for integrated screening and management strategies for PRISm patients to address their broader multimorbid risk profile.

Shareable abstract

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Introduction

Preserved ratio impaired spirometry (PRISm) is a distinct spirometric pattern characterised by a reduction in forced expiratory volume in 1 s (FEV₁) (<80% predicted) with a preserved FEV₁/forced vital capacity (FVC) ratio (≥0.70) [1]. PRISm has gained increasing attention as an intermediate lung function abnormality that may progress to chronic obstructive pulmonary disease (COPD) or remain a stable entity associated with worse respiratory outcomes and higher mortality rates than those in individuals with normal spirometry [2–5]. While its clinical significance is increasingly recognised, the systemic consequences of PRISm, particularly its associations with metabolic and cardiovascular comorbidities, remain incompletely understood [6].

Metabolic disorders, including diabetes mellitus, hypertension, hypercholesterolaemia and cardiovascular disease, are highly prevalent in populations with lung function impairment [7–9]. Prior studies suggest that PRISm is strongly linked to obesity, systemic inflammation and insulin resistance, but whether or not there is an independent association with cardiometabolic comorbidities has been controversial [2, 10]. Understanding the relationship between PRISm and cardiometabolic disease is crucial, because metabolic dysfunction may contribute to pulmonary vascular disease, impaired ventilatory efficiency and increased cardiovascular mortality [11]. Given that cardiometabolic disorders are modifiable risk factors, their early identification and management in PRISm populations could have significant clinical implications [12].

Recent advances in respiratory medicine have highlighted the need for a holistic, multimorbidity-focused approach when assessing chronic lung disease [13]. PRISm is increasingly recognised as part of a broader systemic disease process, rather than an isolated respiratory abnormality [14]. Emerging evidence suggests that shared inflammatory, metabolic and vascular pathways may contribute to both lung function impairment and associated comorbidities [15]. The shift toward integrated care models in respiratory medicine underscores the importance of identifying at-risk individuals early, optimising metabolic health and targeting modifiable risk factors to improve long-term outcomes. However, PRISm remains underdiagnosed, and current guidelines offer limited guidance on its management, necessitating further research to inform clinical practice [16].

In this systematic review and meta-analysis, we aim to comprehensively evaluate the association between PRISm and cardiometabolic comorbidities, including diabetes mellitus, hypertension, hypercholesterolaemia, ischaemic heart disease and heart failure, compared to individuals with normal spirometry. By synthesising available data, this study provides clinically relevant insights into the systemic associations of PRISm and its role in cardiometabolic disease progression, with potential implications for screening, risk stratification and targeted interventions in affected individuals.

Methods

This systematic review is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [17]. The review was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD42024599355). Ethics approval was not required, as determined by the Medical Research Council and Health Research Authority decision tool, in line with the UK Policy Framework for Health and Social Care Research.

Data sources and study selection

Literature searches were conducted for studies published up to 28 October 2024. Searches were performed across PubMed, Embase (accessed via Ovid) and Web of Science Core Collection. The search strategy combined themes of “preserved ratio impaired spirometry” and comorbidities such as “diabetes”, “hypertension”, “cardiovascular disease”, “hypercholesterolaemia”, “ischaemic heart disease”, “heart failure” and “atrial fibrillation”. Each theme was expanded with synonyms, and the full search strategy is detailed in supplementary table S1.

After duplicate removal, two reviewers (RC, DC) independently screened records for inclusion based on predefined criteria (supplementary table S2), using a web-based screening tool [18]. Disagreements were resolved by consensus or discussion with a third reviewer (JJ). Only original research articles published in the English language were considered, with no restriction on study design or study location. Studies were included if they reported both PRISm (defined as FEV₁ <80% of predicted with a preserved FEV₁/FVC ratio ≥0.7) and normal spirometry cohorts from the same study population. Studies where the title included the terms “restrictive spirometry” or “GOLD-unclassified” (Global Initiative for Obstructive Lung Disease (GOLD)) were also assessed for eligibility and included if the spirometry criteria met that of PRISm. Following full-text retrieval, records were reassessed for inclusion and bibliographies were reviewed for further suitable records. Studies were included if they reported the prevalence of one or more of the following comorbidities: diabetes, hypertension, ischaemic heart disease, hypercholesterolaemia, heart failure, atrial fibrillation or cardiovascular disease. Studies that excluded patients based on the pre-existing diagnosis of a specific comorbidity were not included in our analysis.

Data extraction and quality assessment

The included records were quality assessed using the Newcastle-Ottawa Quality Assessment Scale by two independent reviewers (RC and DC) [19]. Data extraction was performed by one reviewer (RC) and independently reviewed for discrepancies between the extracted data and the original study reports by a second reviewer (DC). Disagreements were resolved by consensus. Extracted data included study characteristics (year of publication, study location, population size, and sizes of PRISm and normal spirometry cohorts), reported comorbidities with their prevalence in each cohort, and odds ratios when available. Where studies reported comorbidity prevalence as a percentage of population, the raw number was calculated using the percentage and population size. Additionally, the mean±sd of body mass index (BMI) was collected for each study for normal and PRISm populations.

Data analysis

A narrative synthesis was conducted to describe study and population characteristics and the prevalence of reported comorbidities. The primary outcome was the prevalence of specific comorbidities in PRISm cohorts compared to in normal spirometry cohorts. For studies reporting multiple comorbidities, each was analysed separately.

Meta-analyses were performed for comorbidities reported in at least three included studies; this threshold was chosen to ensure a broad spectrum of evidence while maintaining statistical reliability. When overlapping study populations were identified, the study with the largest sample size or most comprehensive reporting was included, while others were excluded from the meta-analysis to prevent duplicate publication bias.

Heterogeneity was assessed using the I² statistic, with values of 25%, 50% and 75% interpreted as low, moderate and high heterogeneity, respectively [20]. A random-effects model was used for meta-analyses due to expected between-study variability. Influence analysis was performed by iteratively removing each study to assess its impact on pooled estimates, ensuring robustness of results.

Measures of effect size

Where available, studies with published adjusted odds ratios (ORs) were included in a narrative synthesis. If event rates were published or could be calculated from available data, ORs were calculated manually. These were compared with ORs derived from meta-analysis in the narrative synthesis. The continuous measure BMI was analysed as mean difference (MD). All statistical analyses and visualisations were conducted in R (www.r-project.org). Meta-analysis was performed using the “metafor” package (version 4.8-0).

Results

Study selection

We identified 508 potentially relevant studies. After removing duplicates and screening abstracts, 55 full-text articles were reviewed. Following detailed eligibility assessment and expert review, 18 studies met the inclusion criteria for narrative synthesis, and 14 were included in the meta-analysis (figure 1).

FIGURE 1.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart detailing results of initial database searches and subsequent record exclusion at each screening stage.

Study characteristics

The characteristics of the included studies are summarised in table 1. Among the 18 included studies, nine were conducted in East Asia (four from Japan, three from Korea and two from China). Three studies originated from Europe (Sweden, the Netherlands and Austria), while two were from the UK. Additional studies included two populations from the USA and one from Canada. The studies were published between 2011 and 2024, with study populations ranging from 1202 to 353 515 participants.

TABLE 1.

Summary of study characteristics of the 18 included studies examining the association between PRISm and metabolic comorbidities

Study	Study country	Study name	Study type	Sample size (n)	Population	Pre/Post-BD	Age range (years)	Comorbidities included
Kaufmann et al. (2024) [21]	Austria	–	Cross-sectional	9466	General	Pre-BD	40–80	DM, HTN, HF, IHD
Krishnan et al. (2023) [22]	Canada	CanCOLD	Cohort	1561	General	Pre-BD	50–75	DM, HTN
Zhang et al. (2024) [23]	China	–	Cohort	6994	General	Pre-BD	40–70	DM, HTN
Lei et al. (2024) [24]	China	–	Cross-sectional	50 991	General	Pre-BD	40–78	DM, CVD
Miura et al. (2023) [25]	Japan	–	Cross-sectional	11 246	General	Pre-BD	35–65	CVD
Kawatoko et al. (2024) [26]	Japan	–	Cohort	1202	General	Pre-BD	65+	DM, HTN, HC
Tanabe et al. (2022) [27]	Japan	Hospital checkup	Cross-sectional	10 828	General	Pre-BD	40–75	DM, HTN, HC
Washio et al. (2022) [28]	Japan	Hisayama study	Cohort	3032	General	Pre-BD	50–85	DM, HTN, HC
Kim et al. (2022) [29]	Korea	KNHANES	Cross-sectional	17 515	General	Pre-BD	40–80	DM, HTN, HC, IHD
Heo et al. (2020) [30]	Korea	KNHANES	Cross-sectional	27 824	General	Pre-BD	40–80	DM, HTN, HC
Park et al. (2015) [31]	Korea	KNHANES 4 & 5	Cross-sectional	16 151	General	Pre-BD	40–85	DM, HTN, HC
Wijnant et al. (2020) [32]	Netherlands	Rotterdam study	Cohort	5487	General	Pre-BD	45–80	DM, HTN, HF, IHD
Torén et al. (2024) [33]	Sweden	–	Cross-sectional	28 555	General	Pre-BD	50–64	DM, IHD
He et al. (2021) [34]	UK	ELSA cohort	Cohort	6616	General	Pre-BD	50–75	CVD
Higbee et al. (2022) [35]	UK	UK Biobank	Cohort	353 515	General	Pre-BD	40–75	DM, HTN, IHD
Wan et al. (2011) [36]	USA	COPDGene	Cohort	2287	Smokers	Post-BD	40–75	DM, HTN, HF
Wan et al. (2014) [1]	USA	COPDGene	Cohort	10 192	Smokers	Post-BD	40–75	DM, HTN, HF, IHD
Wan et al. (2021) [2]	USA	NHLBI pooled cohorts	Cohort	53 701	General	Pre-BD	18–102	DM, HTN, HF, IHD, HC

BD: bronchodilator; CanCOLD: Canadian Cohort Obstructive Lung Disease; COPDGene: Genetic Epidemiology of COPD; CVD: cardiovascular disease; DM: diabetes mellitus; ELSA: English Longitudinal Study of Aging; HC: hypercholesterolaemia; HF: heart failure; HTN: hypertension; IHD: ischaemic heart disease; KNHANES: Korea National Health and Nutrition Examination Survey; NHBLI: National Heart, Lung, and Blood Institute.

The gender distribution varied across studies, with the proportion of female participants ranging from 27.3% to 61.6%. The mean age of participants ranged from 43.9 to 73.1 years, reflecting differences in the inclusion criteria of the studies. The percentage of never-smokers varied from 30.70% to 74.10%.

Assessment of risk of bias

Two independent reviewers evaluated the methodological quality of the included studies using the Newcastle-Ottawa Quality Assessment Scale. All 18 studies were rated as either moderate or high quality, and none were excluded based on risk of bias. Common limitations identified included reliance on self-reported comorbidity diagnoses and insufficient information on participant follow-up duration (table 2).

TABLE 2.

Quality of included observational studies assessed using the Newcastle-Ottawa Scale

Study	Selection	Comparability	Outcome	Consensus score
He et al. (2021) [34]	2	3	2	7
Heo et al. (2020) [30]	2	2	2	6
Higbee et al. (2022) [35]	3	3	2	8
Kaufmann et al. (2024) [21]	2	3	2	7
Kawatoko et al. (2024) [26]	2	2	3	7
Kim et al. (2022) [29]	2	3	2	7
Krishnan et al. (2023) [22]	3	3	2	8
Lei et al. (2024) [24]	2	3	2	7
Miura et al. (2023) [25]	2	3	3	8
Park et al. (2015) [31]	2	2	2	6
Tanabe et al. (2022) [27]	2	2	2	6
Torén et al. (2024) [33]	2	3	2	7
Wan et al. (2011) [36]	2	3	2	7
Wan et al. (2014) [1]	2	3	2	7
Wan et al. (2021) [2]	3	3	3	9
Washio et al. (2022) [28]	2	3	2	7
Wijnant et al. (2020) [32]	2	3	3	8
Zhang et al. (2024) [23]	3	2	2	7

The Newcastle-Ottawa Scale evaluates three domains: Selection (representativeness of cases, selection of controls, exposure ascertainment), Comparability (control for confounders) and Outcome (assessment method, follow-up duration and completeness). The Consensus score represents the lowest score between two independent reviewers. Studies scoring 7–9 were considered high quality, 4–6 moderate quality, and 0–3 low quality.

Qualitative analysis

The prevalence of diabetes was reported in 16 studies, ranging from 5.6% to 36.5% in PRISm populations and 3.8% to 21.7% in normal spirometry [1, 2, 21–24, 26–33, 35, 36]. Diabetes was more prevalent in PRISm across all studies, except for Zhang et al. (5.6%) [23]. The highest prevalence was observed in Japanese PRISm patients (36.5%), with East Asian cohorts reporting higher rates than European studies. Hypertension was reported in 14 studies, with prevalence ranging from 23% to 81.4% in PRISm and 14.1% to 71.5% in normal spirometry [1, 2, 21–23, 26–32, 35, 36]. The highest rate was in a Dutch PRISm population (81.4%), while other European and North American studies reported lower estimates. Hypertension was more prevalent in PRISm, except in Zhang et al. [23].

Hypercholesterolaemia was examined in seven studies, with prevalence ranging from 18.2% to 74.5% in PRISm versus 16.7% to 58.4% in normal spirometry populations [1, 26–31]. Japanese cohorts reported the highest prevalence, where lipid disorders were more frequently diagnosed than in Korean populations. PRISm was associated with higher hypercholesterolaemia prevalence across all studies.

The prevalence of cardiovascular or cardiac disease was reported as a composite comorbidity in three studies and estimates ranged from 1.92% to 23.4% in PRISm populations compared to 1.5% to 15.4% in populations with normal spirometry [24, 25, 34]. Ischaemic heart disease was assessed in seven studies, with prevalence ranging from 3.6% to 13.5% in PRISm and 1.3% to 8.0% in normal spirometry [1, 2, 21, 29, 32, 33, 35]. Heart failure was reported in five studies, with prevalence ranging from 1.2% to 7.7% in PRISm populations [1, 2, 21, 32, 36]. All studies showed higher heart failure prevalence in PRISm, suggesting a link between lung function impairment and cardiovascular dysfunction.

BMI was reported in 15 studies and was generally higher in PRISm populations [1, 2, 21–23, 25–29, 32–36]. However, Zhang et al. [23] and Washio et al. [28] found higher BMI in normal spirometry groups. The largest BMI differences favouring PRISm were seen in North American studies, while smaller but consistent differences were noted in European and Asian cohorts. This supports the links between PRISm, metabolic dysregulation and systemic inflammation.

Meta-analysis

Meta-analysis was performed to quantify the association between PRISm and cardiometabolic comorbidities (figure 2). A total of 14 studies were included in the meta-analysis, while four were excluded due to potential population overlap. Zhang et al. [23] was excluded due to concerns regarding overlap with Lei et al. [24]. Similarly, Heo et al. [30] and Park et al. [31] were excluded because they examined the same population as Kim et al. [29]. Finally, Wan et al. [36] was excluded in favour of the later study by the same authors [1], which provided a more updated and comprehensive analysis of the same cohort. Unadjusted pooled effect sizes were calculated for diabetes (OR 2.08, 95% CI 1.78–2.42, n=12 studies), hypertension (OR 1.78, 95% CI 1.55–2.03, n=10 studies), ischaemic heart disease (OR 2.05, 95% CI 1.59–2.64, n=7 studies), heart failure (OR 2.82, 95% CI 1.40–5.67, n=4 studies) and hypercholesterolaemia (OR 1.46, 95% CI 1.16–1.85, n=5 studies). PRISm was also associated with higher BMI (MD 1.49 kg·m⁻², 95% CI 0.92–2.05 kg·m⁻²). There were insufficient studies to conduct a meta-analysis on associations between PRISm and generic cardiovascular disease.

FIGURE 2.

Forest plot showing the odds ratios (ORs) with 95% confidence intervals (CIs) for the association between preserved ratio impaired spirometry (PRISm) and included comorbidities compared to individuals with normal spirometry. All study ORs are univariable and unadjusted, calculated using prevalence data reported within the study. Unadjusted pooled ORs for each comorbidity are shown in bold.

Across the included studies, diabetes was consistently more prevalent in PRISm populations, with individual study ORs ranging from 1.44 to 2.82. Hypertension was also significantly associated with PRISm, with study-specific ORs between 1.29 and 2.76. Associations with ischaemic heart disease and with heart failure showed similar trends, with ORs ranging from 1.28 to 3.03 and from 1.66 to 3.97, respectively. Hypercholesterolaemia also showed significant association with PRISm, with ORs between 1.17 and 2.08. The pooled estimates demonstrated a consistent trend of increased prevalence of cardiometabolic comorbidities in PRISm populations across different study settings (figure 2). The association between PRISm and BMI was also consistently observed, with individual studies reporting standardised MDs ranging from 0.30 to 2.90 (supplementary figure S1).

Subgroup analyses (supplementary table S3) demonstrated variation in effect estimates by geography and study design. For BMI, MDs were greatest in North America (MD 2.67, 95% CI 2.22–3.13), followed by Europe (MD 1.68, 95% CI 0.83–2.53) and Asia (MD 0.65, 95% CI 0.15–1.15), with significant heterogeneity between regions (p<0.001). Regional differences were also observed for comorbidities such as ischaemic heart disease (p=0.001), with higher odds in European studies (OR 2.24, 95% CI 1.56–3.22). Stratification by study design showed stronger and more precise associations in cohort studies across outcomes. For example, cohort studies reported higher pooled odds for hypertension (OR 1.86 versus 1.61) and diabetes (OR 2.18 versus 2.09) compared to cross-sectional studies, though between-group differences were modest.

Measures of heterogeneity

Heterogeneity was assessed using the I² statistic. Significant heterogeneity was observed in all meta-analyses: diabetes (I²=88.5%, p<0.0001), hypertension (I²=90.8%, p=0.0003), ischaemic heart disease (I²=77.2%, p=0.0002), heart failure (I²=89.2%, p<0.0001) and hypercholesterolaemia (I²=74.1%, p=0.0039). The high I² values indicate that a high proportion of total variance was due to variation between studies.

Prediction intervals were calculated to estimate the range of true effects, accounting for heterogeneity. The diabetes meta-analysis prediction interval ranged from 1.28 to 3.36, suggesting variability in effect sizes across different populations. Similarly, hypertension showed a prediction interval of 1.21 to 2.60, while ischaemic heart disease had a prediction interval of 1.08 to 3.88. These findings indicate that despite overall positive associations, there is variability in the magnitude of effect sizes reported across studies.

Assessment of publication bias

There was no clear evidence of publication bias based on Egger's test (supplementary table S4). Intercepts for key outcomes including diabetes (p=0.30), hypertension (p=0.22) and BMI (p=0.28) were nonsignificant, suggesting symmetry in effect size distribution. Although intercepts for heart failure and hypercholesterolaemia appeared elevated, these did not reach significance and should be interpreted with caution owing to limited study numbers. Visual inspection of funnel plots (supplementary figures S2–7) supported these findings.

Influence analysis

Influence analysis was conducted using a leave-one-out sensitivity approach. The pooled ORs remained stable across all iterations, ranging from 1.49 to 2.09, with a mean pooled OR of 1.73. No single study had a disproportionate effect on the overall meta-analysis estimates, indicating robustness of the findings. These results suggest that the association between PRISm and cardiometabolic comorbidities is consistent across different studies and is not driven by any one outlier study, reinforcing the reliability of the pooled effect estimates.

Comparison with studies reporting adjusted OR

Available study-reported adjusted ORs, though insufficient for meta-analysis, closely aligned with our unadjusted pooled OR estimates. Four studies reported adjusted ORs for cardiometabolic comorbidities, accounting for key confounders such as age, sex, smoking status and BMI. The associations between PRISm and diabetes, hypertension, hypercholesterolaemia and ischaemic heart disease remained significant after adjustment, with individual study adjusted ORs ranging from 1.20 to 3.46 (table 3). The consistency between adjusted and pooled unadjusted estimates reinforces the robustness of the associations observed, despite study heterogeneity (table 3).

TABLE 3.

Pooled unadjusted odds ratios for each comorbidity generated from meta-analysis compared with each study-reported adjusted odds ratios

	Effect size (95% CI)
Diabetes
Pooled unadjusted OR	2.08 (1.78–2.42)
Torén et al. (2024) [33] aOR	1.91 (1.63–2.24)
Kim et al. (2022) [29] aOR	1.51 (1.29–1.78)
Wan et al. (2021) [2] aOR	1.36 (1.28–1.46)
Wan et al. (2011) [36] aOR	1.58 (1.01–2.45)
Hypertension
Pooled unadjusted OR	1.78 (1.55–2.03)
Kim et al. (2022) [29] aOR	1.31 (1.14–1.50)
Wan et al. (2021) [2] aOR	1.40 (1.31–1.49)
Hypercholesterolaemia
Pooled unadjusted OR	1.46 (1.16–1.85)
Kim et al. (2022) [29] aOR	1.20 (1.04–1.37)
Ischaemic heart disease
Pooled unadjusted OR	2.05 (1.59–2.64)
Torén et al. (2024) [33] aOR	2.20 (1.61–3.0)
Wan et al. (2021) [2] aOR	1.31 (1.18–1.46)
Kim et al. (2022) [29] aOR	1.58 (1.13–2.22)
Heart failure
Pooled unadjusted OR	2.82 (1.48–5.67)
Wan et al. (2021) [2] aOR	1.37 (1.23–1.53)
Wan et al. (2011) [36] aOR	3.46 (1.10–10.81)

aOR: adjusted odds ratio; OR: odds ratio.

Discussion

PRISm is increasingly recognised as a distinct lung function impairment with systemic implications [14, 37]. This systematic review and meta-analysis demonstrates a significant association between PRISm and cardiometabolic comorbidities, including diabetes mellitus, hypertension, hypercholesterolaemia, ischaemic heart disease and heart failure. Across the included studies, PRISm populations consistently exhibited a higher prevalence of these conditions compared to populations with normal spirometry. Meta-analysis confirmed these associations, with pooled ORs indicating increased odds of cardiometabolic disease in PRISm. Sensitivity and influence analyses further validated the robustness of the findings, with no single study disproportionately affecting the pooled estimates. These results reinforce the need to consider PRISm beyond an isolated pulmonary impairment and instead as part of a broader multimorbid disease spectrum.

Previous cohort studies and population-based analyses have suggested an association between PRISm and cardiometabolic disease, but findings have varied. Some studies reported strong associations with diabetes and hypertension, while others indicated that adjustments for BMI and smoking attenuated these relationships [2, 32]. Our meta-analysis consolidates existing data and demonstrates a consistent association across diverse populations. Importantly, our findings align with prior research highlighting the increased cardiovascular risk in individuals with lung function impairments, even in the absence of overt obstructive or restrictive spirometry patterns [3, 6]. Additionally, while COPD and restrictive lung disease have been well established as risk factors for cardiovascular disease, PRISm remains under-recognised in clinical guidelines despite evidence of its systemic associations [38].

The pathophysiological mechanisms underpinning the observed associations are likely multifactorial. Systemic inflammation and oxidative stress, common pathways in both lung impairment and cardiometabolic disease, may contribute to endothelial dysfunction, insulin resistance and vascular remodelling [11]. Chronic low-grade inflammation, driven by adiposity and pulmonary dysfunction, has been implicated in both PRISm and metabolic syndrome, suggesting a bidirectional relationship [39]. Obesity-related mechanical and inflammatory effects may also play a role in PRISm development [1, 6]. Increased BMI and central adiposity are known to reduce lung compliance and functional residual capacity, potentially contributing to PRISm pathophysiology [39]. Additionally, metabolic dysregulation may lead to vascular stiffness and impaired oxygen delivery, further exacerbating pulmonary function decline. The interplay between metabolic dysfunction and lung impairment underscores the importance of integrated management strategies addressing both pulmonary and systemic health. Emerging evidence suggests that genetic predisposition and epigenetic modifications [40] may also contribute to the overlap between PRISm and cardiometabolic syndromes. Shared genetic risk loci associated with lung function impairment and metabolic traits have been identified, pointing toward common biological pathways [1, 40]. Additionally, the gut–lung axis has been proposed as a potential mechanistic link, wherein alterations in gut microbiota composition may drive systemic inflammation, insulin resistance and pulmonary dysfunction [41]. Further research into these evolving areas may provide novel insights into therapeutic targets and intervention strategies.

The increasing global prevalence of metabolic syndromes, obesity and cardiovascular disease necessitates a more comprehensive approach to lung health assessment [13, 15]. PRISm is frequently overlooked in clinical practice and often regarded as a transient or clinically insignificant finding. However, our results suggest that PRISm may serve as an early indicator of systemic disease burden, warranting proactive screening for cardiometabolic comorbidities [42]. Recognising PRISm as part of a multimorbid phenotype may facilitate earlier intervention strategies, including lifestyle modifications, weight management and targeted cardiometabolic therapies [43]. Current pulmonary function guidelines primarily focus on obstructive and restrictive ventilatory defects, with limited consideration of PRISm. Given the significant cardiometabolic burden identified in PRISm populations, routine lung function assessment in at-risk individuals, such as those with metabolic syndrome or cardiovascular risk factors, may be warranted. Similarly, PRISm patients could benefit from structured cardiometabolic screening and cardiovascular risk stratification. Future guidelines should incorporate PRISm as a clinically relevant entity, emphasising its role in systemic disease pathways.

This study represents the most comprehensive synthesis of evidence on PRISm and cardiometabolic comorbidities to date, incorporating data from multiple regions and diverse populations. The inclusion of both crude and adjusted estimates strengthens the validity of our findings. Additionally, the influence analysis confirmed that no single study disproportionately impacted the pooled results, reinforcing the reliability of the observed associations. However, several limitations should be acknowledged. First, heterogeneity across studies was notable, with variations in study design, inclusion criteria and resulting population characteristics. Although subgroup analyses aimed to address key differences, the estimated effect sizes are still highly likely to be influenced by residual confounding because our pooled results are unadjusted. Second, the cross-sectional nature of some included studies limits causal inference, highlighting the need for longitudinal research to assess temporal relationships between PRISm and cardiometabolic disease. All included studies were conducted in high-income or upper-middle-income settings and the relevance in the global south requires further attention [44]. Finally, despite our thorough assessment, potential publication bias remains a consideration, because studies reporting nonsignificant associations may be under-represented in the literature.

PRISm is strongly associated with an increased prevalence of cardiometabolic comorbidities, reinforcing its role as a systemic condition rather than a purely pulmonary abnormality. These findings have important implications for clinical practice, emphasising the need for integrated screening and management strategies. Future research should focus on longitudinal cohort studies to further establish causal relationships, mechanistic investigations into shared disease pathways and interventional trials assessing the impact of cardiometabolic control on PRISm progression. Recognising PRISm as a key component of multimorbidity frameworks may improve long-term outcomes and inform evidence-based guideline development.

Supplementary material

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Supplementary material

00396-2025.supplement