Hypertension is a major risk factor for CVD, particularly ischemic heart disease and stroke. It has become a leading cause of global mortality and disability-adjusted life years (1–3). In 2010, about 31.1% of the global adult population (1.39 billion) had hypertension, comprising nearly 10% of worldwide healthcare spending (4). However, hypertension is not accompanied by obvious relevant symptoms, and patients can have hypertension without knowing it (5). Indeed, hypertension frequently coexists with a broader spectrum of anthropometric and metabolic abnormalities, encompassing abdominal (visceral) obesity, characteristic dyslipidemia (low high-density lipoprotein cholesterol and elevated triglyceride levels), glucose intolerance, insulin resistance, and hyperuricemia (6). Therefore, early identification and prevention of hypertension, along with comprehending its association with metabolic components is an essential issue.

Insulin resistance (IR) is closely linked to the substantial development and progression of diabetes (7–9). At present, the high insulin normoglycemic clamp (HEC) stands as the gold standard for assessing insulin sensitivity in peripheral tissues (10). Nonetheless, this invasive approach is intricate, time-consuming, and technically demanding, leading to the common preference for simpler indicators to assess insulin resistance. Traditional tools such as the homeostatic model assessment for IR (HOMA-IR) and quantitative insulin sensitivity check index (QUICKI), which use fasting insulin levels to measure insulin resistance, face practical limitations and variability (11). In addition to other insulin resistance assessment tools that do not require fasting insulin levels, including the product of glucose and triglycerides (TyG index), the product of glucose, triglycerides, and body mass index (TyG-BMI index), and the ratio of triglycerides divided by high density lipoprotein-cholesterol (TG/HDL-C ratio) (12–14), METS-IR emerges as an innovative tool for estimating insulin resistance. It employs readily available primary care parameters: fasting blood glucose (FBG), triglycerides (TG), high density lipoprotein-cholesterol (HDL-C), and body mass index (BMI). This approach eliminates the need for costly and variable fasting insulin tests. As a simple, indirect method, METS-IR identifies insulin resistance and corresponds with the underlying pathophysiological factors of metabolic syndrome, including obesity, dyslipidemia, hyperglycemia and intra-abdominal fat accumulation. Consequently, METS-IR also emerges as a promising metric for evaluating cardiometabolic risk (10, 15–17).

However, there are only few studies on the association between the METS-IR index and hypertension, with studies limited only to China, Mexico and Japan (15, 16, 18–22). The association between METS-IR and hypertension in the US population remains unclear. In this cross-sectional study, we aimed to explore the association between METS-IR and hypertension using data from the National Health and Nutrition Examination Survey (NHANES).

In this population-based, cross-sectional study, we explored the association between METS-IR, an new tool for estimating insulin resistance, and hypertension. After adjusting for potential confounders, our results showed that METS-IR, either as a continuous or a categorical variable, was significantly associated with hypertension among US adults. This positive association also extended to the level of SBP and DBP. The subgroup analyses yielded similar results. Through RCS regression analysis, we observed a non-linear dose-response relationship between METS-IR and hypertension risk. The minimal thresholds for the beneficial association of METS-IR with hypertension, SBP and DBP were all 46.88. Our results indicate that METS-IR, readily assessable in primary care settings, could be an effective early marker for hypertension, contributing to primary prevention strategies.

Many studies have found that hypertension and insulin resistance were closely related and mutually causal. The well-established bidirectional link between hypertension and insulin resistance is substantiated by Wang et al.’s meta-analysis. This study found that elevated fasting insulin levels or insulin resistance, quantified using HOMA-IR, correlate with an increased risk of hypertension in the general population. Notably, individuals with the highest fasting insulin and HOMA-IR levels showed a 54% and 43% increased risk, respectively, of developing hypertension (30). Lin et al.’s study demonstrated that individuals with hypertension experienced a more significant increase in HOMA-IR over five years (ΔHOMA2-IR/5 yr) compared to non-hypertensive individuals (adjusted p = 0.044). Furthermore, those with treated hypertension were at the highest risk of developing diabetes, as evidenced by a hazard ratio (HR) of 2.98 (p < 0.001), and exhibited the greatest change in ΔHOMA2-IR/5 yr relative to those with normal blood pressure (31). Although some studies have identified a relationship between insulin resistance or hyperinsulinemia and hypertension, others, such as Ferrannini et al., have reported more ambiguous associations (32). Specifically, their study involving 2,241 normotensive, nondiabetic individuals showed that a substantial increase in plasma insulin concentration (200 μU/mL) was associated with only a minimal rise in blood pressure (BP) of 1 mm Hg. Moreover, other researchers have observed similar plasma insulin levels in normotensive, nondiabetic individuals and those with hypertension, hinting at a possible inverse relationship between insulin and BP (33). Furthermore, in various types of secondary hypertension not linked to obesity, such as renovascular or mineralocorticoid-induced hypertension, there is no evidence to suggest the presence of insulin resistance (34). These findings indicate that hyperinsulinemia or insulin resistance alone cannot fully account for a direct relationship with hypertension.

However, the metabolic outcomes of insulin resistance, including obesity, hyperglycemia, and dyslipidemia, may exacerbate hypertension (33). A novel measure for evaluating insulin resistance, named METS-IR, has been developed. This metric is calculated from fasting levels of glucose, triglycerides, HDL-C, and BMI. Crucially, METS-IR integrates aspects of obesity and metabolic syndrome and avoids dependence on fasting insulin levels (10). Several studies have investigated the relationship between METS-IR and hypertension. Han et al.’s research in normoglycemic individuals from Gifu, Japan, identified a significant link between high METS-IR and both pre-hypertension (adjusted OR = 1.95, 95% CI: 1.61–2.36) and hypertension (adjusted OR = 2.12, 95% CI: 1.44–3.11), persisting even after adjusting for confounders in multivariable logistic regression. Furthermore, when considering METS-IR as a continuous variable, each unit increase was associated with a 7% rise in pre-hypertension (adjusted OR = 1.07, 95% CI: 1.06–1.08) and a 13% increase in hypertension (adjusted OR = 1.13, 95% CI: 1.10–1.16). Stratified analyses showed a positive correlation between METS-IR and both pre-hypertension and hypertension across diverse normoglycemic subgroups (22). However, as the study data were sourced from Japanese subjects, the applicability of the findings to other ethnic groups remains uncertain. Additionally, this research, being a secondary analysis of pre-existing data, lacks clarity on the specific procedures used during medical consultations, such as the methodology for measuring blood pressure. Xu et al. explored the association between the METS-IR and hypertension in the non-overweight Chinese population. They observed a significant increase in the risk of developing hypertension in the third quartile group (HR 1.58, 95% CI 1.12–2.22), with an even higher risk in the fourth quartile group (HR 1.96, 95% CI 1.40–2.76), compared to the lowest quartile of METS-IR. The study identified a linear dose-response relationship between METS-IR and hypertension risk (HR 1.08, 95% CI 1.04–1.12) (35). The study failed to address key lifestyle influences like physical activity, alcohol consumption, and smoking, which are significant for blood pressure. Additionally, its findings, based primarily on Chinese participants, may not extend to other ethnic groups. Li et al. discovered that METS-IR served as a potent predictor of CVD and its subtypes among patients with hypertension and obstructive sleep apnea, thereby aiding in the identification of high-risk individuals and offering personalized CVD prevention strategies (15). In another study conducted by the same researchers, it was suggested that there exists an association between METS-IR and the risk of both overall stroke and ischemic stroke specifically among patients with hypertension (16). These two studies primarily concentrated on assessing the cardiovascular and cerebrovascular risks associated with METS-IR among patients with hypertension.

Our study, however, focused on exploring the association between METS-IR and hypertension among adults aged 20 years and older in the US, utilizing data from NHANES. We excluded BMI and waist circumference to avoid co-linearity with METS-IR, while included diabetes and CVD due to their known associations with hypertension. In our fully adjusted model, accounting for diabetes and CVD, we still found a robust association between METS-IR and hypertension risk, as well as SBP and DBP, even though prior research had already established the connections between METS-IR and both diabetes and CVD. Notably, our study reveals a dose-response relationship between METS-IR and hypertension, marking a significant finding in understanding hypertension’s metabolic drivers. We have established specific METS-IR thresholds at 46.88, linked to hypertension, SBP, and DBP, offering clinicians and researchers precise, actionable criteria for early detection and intervention. This development enhances hypertension risk stratification, incorporating METS-IR into assessments for a more refined prediction of hypertension risk. Such integration paves the way for personalized and more effective prevention strategies. Beyond its immediate findings, our study paves the way for future research into the mechanisms by which METS-IR influences hypertension. It opens up new avenues for exploring potential interventions that could mitigate this risk, thereby contributing to the broader goal of reducing the global burden of hypertension.

Pathophysiological evidence supports a link between METSIR and hypertension. Metabolic outcomes of insulin resistance may lead to hypertension through various mechanisms, including adipokines from fat tissue, altered gut microbiota, sympathetic nervous system (SNS) activation, imbalance in antinatriuretic and natriuretic hormones, and dysfunction in vascular and renal systems (36–39). Both animal and human studies suggest that hypertension in metabolic syndrome arises from factors that increase renal sodium reabsorption, leading to extracellular fluid volume expansion. Notably, three mechanisms are critical in this process: kidney compression by surrounding fat, renin-angiotensin-aldosterone system activation, and heightened SNS activity. Chronic obesity exacerbates hypertension and causes cardiovascular and renal damage, especially in conjunction with metabolic issues like hyperglycemia and hyperlipidemia (40, 41). Further investigation is needed to elucidate the exact underlying mechanisms and enhance our understanding of the pathophysiology of hypertension.

Several advantages can be attributed to our study. A representative sample of the US population is collected in the NHANES from 2007- 2016 based on a well-designed study protocol with extensive quality assurance and quality control. As a second step, we controlled for confounding covariates to ensure that our results are reliable and applicable to a broad range of individuals. We acknowledge, however, that the study has certain limitations. First, as a result of the cross-sectional nature of the study, we could not determine the temporal association between METS-IR and hypertension. Recognizing this limitation, we suggest that future research on the relationship between METS-IR and hypertension should include longitudinal designs. Such studies would enable researchers to track changes over time, providing clearer insights into whether elevated METS-IR levels precede the development of hypertension, thereby offering stronger evidence of a potential causal relationship. Second, because of a lack of data covariates, a large number of participants were excluded, which might cause bias. To mitigate this, we utilized multiple imputation (MI) techniques to address the gaps in our data, followed by a thorough re-analysis of the imputed dataset. Our sensitivity analysis shows that our primary conclusions remain stable, even when considering the potential impact of missing data, thus bolstering our confidence in the robustness of our findings. Third, the study did not eliminate bias from additional potential confounders, like dietary patterns, genetic predispositions, and psychosocial stressors, that were not adjusted for. Last, we recognize that relying on diagnoses derived from databases, instead of direct clinical measurements or diagnoses from medical institutions, introduces potential biases into our study. This limitation stems from the inherent nature of cross-sectional studies, which often depend on previously collected data and may lack the specificity and accuracy of clinical diagnoses. These limitations highlight the importance of conducting future longitudinal studies to investigate these aspects further.

The findings of this cross-sectional study suggest that a higher METS-IR was independently associated with a higher prevalence of hypertension and a higher SBP and DBP. These findings indicate that METS-IR could potentially act as an effective tool for assessing hypertension risk and formulating targeted intervention strategies based on METS-IR levels. However, further longitudinal studies are necessary to validate these findings. Additional research is also needed to uncover the mechanisms through which METS-IR influences hypertension and to identify potential targets for therapy.

Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/.

The studies involving humans were approved by The NCHS Research Ethics Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

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