Association between different insulin resistance surrogates and all-cause mortality in patients with Osteoarthritis: Evidence from a large population-based study

doi:10.21203/rs.3.rs-5232702/v1

Background

Timely identification and intervention of risk factors impacting prognosis are imperative for individuals with Osteoarthritis (OA). However, the relationship between insulin resistance (IR) surrogates and long-term all-cause mortality in patients with OA remains unclear. This study aimed to explore the relationship between different IR surrogates and all-cause mortality and identify valuable predictors of survival status in this population.

Methods

The data came from the National Health and Nutrition Examination Survey (NHANES 2001–2018) and National Death Index (NDI). Multivariate Cox regression and restricted cubic splines (RCS) were performed to evaluate the relationship between homeostatic model assessment of IR (HOMA-IR), triglyceride glucose index (TyG index), triglyceride glucose-body mass index (TyG-BMI index) and all-cause mortality. The segmented regression and Log-likelihood ratio test were conducted to calculate cut-off points when segmenting effects were found. Then, segmented Kaplan–Meier analysis, LogRank tests, and multivariable Cox regression were carried out. Receiver operating characteristic (ROC) and decision curve analysis (DCA) were drawn to evaluate the differentiation and accuracy of IR surrogates in predicting the all-cause mortality. Stratified analysis and interaction tests were conducted according to age, gender, diabetes, cancer, and hypoglycemic drugs or insulin use.

Results

1154 participants were included in the study. During the median follow-up of 124 months, 369 participants died. RCS showed that HOMA-IR had a segmented effect on all-cause mortality. 3.72 was a statistically significant inflection point. When the HOMA-IR was less than 3.72, it was negatively associated with all-cause mortality[HR = 0.78,95%CI (0.64, 0.94),P = 0.011]. Conversely, when the HOMA-IR was greater than 3.72, it was positively associated with all-cause mortality [HR = 1.05,95%CI (1.01, 1.09),P = 0.017]. ROC and calibration curves indicated that HOMA-IR was a reliable predictor of survival status (area under curve = 0.8475). No interactions between HOMA-IR and stratified variables were found.

Conclusion

HOMA-IR display a U-shaped association with all-cause mortality in patients with OA. HOMA-IR was a reliable predictor of all-cause mortality in this population.

Health sciences/Biomarkers

Health sciences/Endocrinology

Health sciences/Medical research

Health sciences/Risk factors

Osteoarthritis

Insulin resistance

All-cause mortality

Nhanes

Osteoarthritis (OA) is currently the most prevalent form of arthritis, impacting approximately 3.3 to 3.6% of the global population. It is ranked as the 11th most incapacitating disease globally, leading to moderate to severe disability in an estimated 43 million individuals [1]. According to the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD) report, OA was identified as one of the most widespread rheumatic musculoskeletal disorders, affecting approximately 303 million people worldwide in 2017 and resulting in an estimated 9,604,000 years lost due to OA-related disability [2]. Patients with osteoarthritis (OA) face a heightened risk of all-cause mortality, particularly in relation to cardiovascular diseases (CVD), which is directly correlated with the severity of disability [3]. The substantial burden of disease associated with OA prompted the Osteoarthritis Research Society International (OARSI) to publish a White Paper in 2016 characterizing OA as a significant illness[4]. Therefore, timely identification and intervention of risk factors impacting prognosis are imperative for individuals with OA.

Insulin resistance (IR) is a common characteristic found in individuals with diagnoses of type 2 diabetes mellitus (T2DM), hypertension, dyslipidemia, and cardiovascular disease (CVD). It signifies a diminished sensitivity to the biological effects of insulin[5]. Although osteoarthritis (OA) was initially believed to be a condition primarily affecting the elderly, additional risk factors beyond age have been recognized as predisposing factors for OA. Recent studies provide compelling evidence suggesting that osteoarthritis (OA) may be considered a metabolic disease, with various components of the metabolic syndrome (MetS) contributing to its pathogenesis and progression [6–8]. Additionally, there is a heightened risk for developing OA with the presence of MetS. Cohort studies have confirmed a robust positive correlation between MetS and the incidence of OA, with a notable increase in OA risk for each additional component of MetS[9]. Interestingly, insulin resistance (IR), a key characteristic of MetS, is recognized as a risk factor for both microvascular and macrovascular complications[10].

The hyperinsulinemic-normal glucose clamp test is regarded as the gold standard for IR measurement, but due to its complexity and invasiveness, it is often deemed unsuitable for clinical research [11]. Recognized alternative indices include the homeostatic model assessment of insulin resistance (HOMA-IR), triglyceride glucose index (TyG index) and triglyceride glucose-body mass index (TyG-BMI index) [12]. While the TyG index has been associated with an elevated risk of OA [13], few studies have evaluated the correlation between these IR surrogates and all-cause mortality in patients with OA. The evidence on which IR surrogates can serve as long-term predictors of all-cause mortality risk in patients with OA still remains unclear.

This study linked the National Health and Nutrition Examination Survey (NHANES) and National Death Index (NDI) data to investigate the relationship between different IR surrogates and long-term all-cause mortality in patients with OA. The aim is to identify valuable predictors of survival status in this population.

Study design

The baseline data was obtained from NHANES. NHANES is a continuous cross-sectional survey with national representation and complex multi-stage sampling, aiming to assess the nutritional and health status of the non-institutionalized US population. The methodological details of NHANES have been extensively documented in previous research [12]. We collected the information of participants who were first interviewed between 2001 and 2018. Then we linked NDI of the National Center for Health Statistics (NCHS) to obtain the survival status of the participants, and constructed a NHANES longitudinal follow-up cohort. The NCHS Ethical Review Board approved the study. All participants provided informed consent before participating in this survey, and the NHANES dataset does not contain identifiable patient characteristics [12]. Therefore, no additional informed consent and ethical review were required for our research.

Study population

Participants with OA who were surveyed between 2001 and 2018 were included in this study. Exclusions were applied to participants lacking complete survival data and IR surrogates. The history of OA was obtained through interviews. OA information was obtained by asking participants: “Which type of arthritis was it?” If they answered "Osteoarthritis" or “Osteoarthritis or degenerative arthritis” to the question, they were diagnosed OA.

IR surrogates

The IR surrogates in this study include the HOMA-IR, TyG index, and TyG-BMI index which are all determined by formula: HOMA-IR = fasting glucose (mmol/L) × fasting insulin (µU/mL)/22.5 [12]; TyG = Ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dl) /2] [13]; TyG-BMI = TyG index × BMI (kg/m2) [15]. The detailed measurement techniques for the research variables can be inspected at www.cdc.gov/nchs/nhanes/, the official website of the Centres for Disease Control and Prevention (CDC).

Ascertainment of mortality and follow‑up

Mortality status was determined by linking NHANES data with records from NDI available at https://www.cdc.gov/nchs/data-linkage/mortality-public.htm. Participants were categorized as deceased or alive based on information obtained from the NDI. Follow-up time was calculated from the date of the NHANES examination to the date of death or December 31, 2019, whichever came first.

Covariates

Demographic, medical history, and laboratory blood test data of participants were collected in NHANES. Demographic data included age, gender, race, education level, marital status, and income -poverty ratio (PIR). Race was categorized as non-Hispanic white, non-Hispanic black, other Hispanic, Mexican American, and other race. Education levels were grouped as “High school and below” or “Above High school”.Medical history information included diabetes, cancer, heart failure, stroke, use of hypoglycemic and lipid-lowering prescription drugs, tobacco use, BMI and waist circumference. The demographic and medical history information was obtained through interviews. Smoking more than 100 cigarettes in a lifetime was defined as a tobacco user. BMI and waist circumference were obtained through measurement. The definition of diabetes was self-reported diagnosis, use of insulin or oral hypoglycemic agents, fasting glucose ≥ 7 mmol/L, or HbA1c ≥ 6.5% [16].Laboratory blood test data included low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), triglyceride (TG), total cholesterol (TC), alanine aminotransferase (ALT), albumin, alkaline phosphatase (ALP), aspartate aminotransferase (AST), serum urea nitrogen, creatinine (Cr), gamma-glutamyltransferase (GGT), lactate dehydrogenase (LDH), iron, phosphorus, potassium, sodium, calcium, total bilirubin, serum uric acid, glycosylated hemoglobin (HbA1c), hemoglobin (Hb), C-reactive protein (CRP), platelet count (PLT) and white blood cell count (WBC).

Statistic analysis

Participants were divided into two groups according to survival status to describe the characteristics of the study population. Continuous variables were expressed as mean and standard deviation or median and quartile, and the t-test or Kruskal–Wallis rank sum test was selected for hypothesis testing according to applicable conditions. Classified variables were expressed as absolute numbers and percentages, and the chi-square test was used for hypothesis testing.

Multivariable Cox regression models were used to evaluate of the linear relationship between different IR surrogates and survival status. We constructed three regression models by adjusting different covariates to control for confounding biases. The selection of covariates was driven both theoretically and statistically. Some covariates theoretically associated with survival status were fixed in the model, such as age, gender, race, diabetes, and cancer. Other variables were selected using statistical methods. Adjust I adjusted for age, gender, race, diabetes, and cancer, and Adjust II adjusted for age, gender, race, diabetes, cancer, marriage, education, HF, stroke, hypoglycemic drugs or insulin use, LDL-C, HDL-C, ALP, serum uric acid, iron, sodium, potassium, calcium, platelet count, white blood cell count and C-reactive protein. Multiple chain interpolation was used to fill in missing data with ‘mice’ package.

Multivariable restricted cubic splines (RCS) were used to identify the nonlinear relationship between different IR surrogates and survival status. We aimed to identify potentially valuable predictors of survival status based on the shape of the RCS curves. Various knot placements between 3 and 7 were tested, with the model featuring the lowest Akaike Information Criterion (AIC) value selected for RCS, ultimately utilizing 4 knots. The segmented regression and Log-likelihood ratio test were used to calculate potential cut-off points. Based on these cut-off points, we conducted segmented Kaplan–Meier analysis, LogRank tests, and multivariable Cox regression [17].

In addition, we evaluated the discrimination ability and accuracy of the fully adjusted model using receiver operating characteristic (ROC) curves, area under the curve (AUC), and Decision Curve Analysis (DCA). We compared the differences in AUC. Finally, we conducted stratified analyses and interaction tests based on age, gender, diabetes, cancer, and the use of hypoglycemic drugs or insulin.

This analysis took into account the complex NHANES sample design by considering appropriate sample weights, stratifications, and clustering. Sample weights for analysis were calculated as Fasting Subsample 2 Year MEC Weight (WTSAF2YR) divided by 9. Data analysis was completed by R software (version 4.4.0). P < 0.05 on both sides was considered statistically significant.

General characteristics of participants

The screening process for the study population is presented in Fig. 1. A total of 1154 participants were included in the analysis and provided follow-up data for a total of 144820 person-months. And during the median follow-up of 124 months, 369 participants died. Compared to the surviving participants, dead participants demonstrated several notable differences. They tended to be older, having diabetics, and a history of cancer and heart failure. In addition, they had lower HOMA-IR and TyG-BMI index at baseline, but higher in TyG index. Detailed information on the demographics, medical history, laboratory tests at baseline, and the results of univariate analysis, are presented in Tables 1 and 2.

Table 1

Demographic and medical history baseline characteristics
Characteristic	Dead participants, N = 369¹	Surviving participants, N = 785¹	P Value
Age.years	77.00 (70.00, 80.00)	61.00 (52.00, 69.00)	< 0.001
Gender			0.4
female	228 (61.79%)	505 (64.33%)
male	141 (38.21%)	280 (35.67%)
Race			< 0.001
Mexican American	19 (5.15%)	70 (8.92%)
Other Hispanic	14 (3.79%)	46 (5.86%)
Non-Hispanic White	299 (81.03%)	509 (64.84%)
Non-Hispanic Black	31 (8.40%)	115 (14.65%)
Other Race	6 (1.63%)	45 (5.73%)
Marriage			0.001
Having a partner	194 (52.57%)	491 (62.55%)
Without partner	175 (47.43%)	294 (37.45%)
Education			< 0.001
High school and below	203 (55.01%)	328 (41.78%)
Above High school	166 (44.99%)	457 (58.22%)
PIR	2.15 (1.24, 3.66)	2.68 (1.37, 4.90)	< 0.001
BMI	27.90 (24.50, 32.24)	29.55 (25.98, 34.10)	< 0.001
Waist circumference.cm	100.20 (91.00, 111.20)	102.50 (93.30, 111.60)	0.051
Diabetics			< 0.001
Yes	183 (49.59%)	299 (38.09%)
No	186 (50.41%)	486 (61.91%)
Cancer patients			< 0.001
Yes	99 (26.83%)	106 (13.50%)
No	270 (73.17%)	679 (86.50%)
Heart failure patients			< 0.001
Yes	51 (13.82%)	22 (2.80%)
No	318 (86.18%)	763 (97.20%)
Stroke patients			0.007
Yes	29 (7.86%)	32 (4.08%)
No	340 (92.14%)	753 (95.92%)
Hypoglycemic drugs or insulin Users			0.003
Yes	152 (41.19%)	253 (32.23%)
No	217 (58.81%)	532 (67.77%)
Lipid-lowering drug users			< 0.001
Yes	274 (74.25%)	455 (57.96%)
No	95 (25.75%)	330 (42.04%)
Tobacco users			0.018
Yes	208 (56.37%)	384 (48.92%)
No	161 (43.63%)	401 (51.08%)
¹Mean(SD) \| Median (Q1-Q3) \| N(%). PIR: income-poverty ratio, BMI: body mass index

Table 2

Baseline characteristics of laboratory blood test
Characteristic	Dead participants, N = 369¹	Surviving participants, N = 785¹	P Value
LDL-C.mmol/L	2.82 (2.28, 3.44)	2.97 (2.46, 3.70)	0.001
HDL-C.mmol/L	1.34 (1.11, 1.73)	1.34 (1.14, 1.66)	0.3
TG.mg/dL	118.00 (88.00, 174.00)	115.00 (80.00, 167.00)	0.082
TC.mmol/L	5.09 (4.40, 5.82)	5.12 (4.45, 5.92)	0.3
Albumen.g/L	41.00 (39.00, 43.00)	42.00 (40.00, 44.00)	0.002
ALP.IU/L	67.00 (56.00, 85.00)	67.00 (55.00, 84.00)	0.6
AST.U/L	23.00 (20.00, 28.00)	23.00 (20.00, 28.00)	0.8
ALT.U/L	18.00 (15.00, 23.00)	21.00 (17.00, 28.00)	< 0.001
Creatinine.umol/L	81.33 (70.72, 101.66)	74.26 (63.65, 88.40)	< 0.001
GGT.IU/L	21.00 (14.00, 32.00)	20.00 (15.00, 31.00)	> 0.9
LDH.IU/L	141.00 (124.00, 160.00)	134.00 (118.00, 150.00)	< 0.001
Serum urea nitrogen.mmol/L	6.07 (4.28, 8.21)	5.00 (3.93, 6.07)	< 0.001
Total bilirubin.umol/L	11.97 (10.26, 15.39)	11.97 (10.26, 15.39)	0.8
Serum uric acid.umol/L	339.00 (279.60, 398.50)	327.10 (273.60, 380.70)	0.019
Iron.umol/L	15.20 (11.30, 18.80)	14.90 (11.60, 19.00)	0.8
Calcium.mmol/L	2.35 (2.30, 2.43)	2.35 (2.30, 2.40)	0.2
Phosphorus.mmol/L	1.23 (1.13, 1.36)	1.20 (1.10, 1.29)	0.002
Potassium.mmol/L	4.20 (3.90, 4.40)	4.00 (3.80, 4.20)	< 0.001
Sodium.mmol/L	139.00 (138.00, 141.00)	140.00 (138.00, 141.00)	0.091
Hemoglobin.g/dL	13.80 (12.60, 14.90)	14.20 (13.30, 15.00)	< 0.001
White blood cell count.1000 Cells /uL	6.90 (5.70, 8.10)	6.40 (5.40, 7.70)	0.002
Platelet count.1000 Cells /uL	237.00 (194.00, 285.00)	248.00 (208.00, 291.00)	0.016
C-reactive protein.mg/dL	0.27 (0.13, 0.62)	0.24 (0.10, 0.53)	0.028
Fasting blood glucose.mmol/L	5.76 (5.31, 6.56)	5.66 (5.26, 6.16)	0.008
Fasting insulin.µU/mL	8.83 (5.76, 13.39)	10.91 (6.70, 17.34)	< 0.001
HbA1c.%	5.70 (5.40, 6.10)	5.60 (5.30, 6.00)	0.026
HOMA-IR	2.31 (1.43, 4.21)	2.75 (1.67, 4.75)	0.004
TyG	8.74 (8.43, 9.19)	8.70 (8.31, 9.07)	0.019
TyG-BMI	245.59 (212.54, 290.89)	257.63 (222.57, 304.09)	0.001
¹Mean(SD) \| Median (Q1-Q3). LDL-C low density lipoprotein cholesterol, HDL-C high density lipoprotein cholesterol, TG triglycerides, TC total cholesterol, ALP alkaline phosphatase, AST aspartate aminotransferase, ALT alanine aminotransferase, GGT gamma-glutamyltransferase, LDH lactate dehydrogenase, HbA1c glycosylated hemoglobin, HOMA-IR homeostatic model assessment of insulin resistance, TyG triglyceride glucose index, TyG-BMI triglyceride glucose- body mass index

Association between different IR surrogates and survival status

The results of the multivariable Cox regression and RCS are presented in Table 3 and Fig. 2, respectively. In the fully adjusted Cox regression model, when IR surrogates were included as continuous variables, their relationship with survival status was not statistically significant. The RCS curve demonstrated the nonlinear relationship between IR surrogates and survival status. Figure 2A showed that the HOMA-IR may have a piecewise effect on survival status with a distinct inflection point, indicating that it may be a predictor of survival status(P for nonlinear = 0.0267).

Table 3

Relationship between different IR surrogates and all-cause mortality
	Non-adjusted			Adjusted I			Adjusted II
Exposure	HR¹	95% CI¹	P Value	HR¹	95% CI¹	P Value	HR¹	95% CI¹	P Value
HOMA-IR	1.01	0.98, 1.04	0.4	1.03	1.00, 1.06	0.030	1.02	0.98, 1.05	0.4
TyG	1.21	0.99, 1.47	0.066	1.15	0.95, 1.39	0.2	1.09	0.85, 1.41	0.5
TyG-BMI	1.00	1.00, 1.00	0.2	1.00	1.00, 1.00	0.2	1.00	1.00, 1.00	> 0.9
¹HR = Hazard Ratio, CI = Confidence Interval

The relationship between HOMA‑IR and survival status

Using segmented regression and Log-likelihood ratio test, we found a statistically significant cut point (cut point = 3.72) in the relationship between the HOMA-IR and survival status. When the HOMA-IR is less than 3.72, it was negatively associated with survival status [HR = 0.80,95%CI (0.66, 0.97)]. Conversely, when the HOMA-IR was greater than 3.72, it was positively associated with survival status [HR = 1.05,95%CI (1.02, 1.09)]. These results are presented in Table 4.

Taking 3.72 as the cut-off point, we further studied the relationship between HOMA-IR and survival status in segments. The results of the segmented Kaplan–Meier analysis are shown in Fig. 3. When the HOMA-IR was less than 3.72, the binary classification of HOMA-IR were associated with low survival rates in individuals with low levels of HOMA-IR (Fig. 3A). However, when the HOMA-IR was greater than 3.72, individuals with high levels of HOMA-IR were associated with low survival rates (Fig. 3B).

In addition, we also convert HOMA-IR into categorical variables for piecewise multivariate Cox regression. The results showed that when the HOMAIR was greater than 3.72, in the Adjust I, the higher HOMA-IR was associated with an increased all-cause mortality rate [HR = 1.86,95%CI (1.17, 2.95)], and the trend test was statistically significant (P = 0.008). These results are presented in Table 5.

Table 4

Cut point and segmentation effects of HOMA-IR
Items	Outcome
Total
Linear effect	1.00(0.96,1.04) > 0.9
Segmentation effect
Cut point(K)	3.72
<K segment effect	0.80(0.66,0.97)0.025
>K segment effect	1.05(1.02,1.09)0.002
P for Log-likelihood ratio	< 0.001
hazard ratio(HR), 95% confidence interval(CI),and P-value. Adjusted for age, gender, race, diabetes, and cancer.

Table 5

Segmented Cox regression analysis and trend test of HOMA-IR
	Non-adjusted	Adjust I	Adjust II
HOMA-IR < 3.72
HOMA-IR dichotomous
Low	Reference	Reference	Reference
High	0.83(0.61, 1.14)0.2	0.78(0.57, 1.07)0.12	0.80(0.56, 1.15)0.2
HOMA-IR quartile
Q1	Reference	Reference	Reference
Q2	1.42(0.88, 2.28)0.15	0.82(0.58, 1.18)0.3	0.88(0.61, 1.27)0.5
Q3	1.02(0.66, 1.58) > 0.9	0.71(0.46, 1.08)0.11	0.77(0.48, 1.23)0.3
Q4	0.95(0.60, 1.50)0.8	0.69(0.45, 1.06)0.091	0.71(0.45, 1.12)0.14
P trend	0.3	0.3	0.5
HOMA-IR > 3.72
HOMA-IR dichotomous
Low	Reference	Reference	Reference
High	1.85(1.21, 2.82)0.005	1.86(1.17, 2.95)0.008	1.64(0.95, 2.83)0.079
HOMA-IR quartile
Q1	Reference	Reference	Reference
Q2	1.09(0.56, 2.10)0.8	1.46(0.76, 2.79)0.3	1.45(0.70, 3.00)0.3
Q3	1.95(1.11, 3.43)0.020	2.00(1.10, 3.65)0.024	1.85(0.91, 3.78)0.090
Q4	1.77(0.99, 3.19)0.056	2.37(1.30, 4.30)0.005	1.98(0.94, 4.18)0.073
P trend	0.046	0.019	0.2
hazard ratio(HR), 95% confidence interval(CI),and P-value.

Subgroup analysis and model evaluation

Table 6 presents the results of the segmented subgroup analysis and interaction tests between the HOMA-IR and survival status. Age, gender, diabetes, cancer, hypoglycemic or insulin use did not have significant interactions with the HOMA-IR. The ROC curve and DCA in Fig. 4 indicated that when using the HOMA-IR to evaluate survival status, the fully adjusted model we constructed had better discriminatory and accuracy compared to the univariate Cox regression model. The AUC value for the fully adjusted model was 0.848, which was significantly higher than unadjusted model and the difference was statistically significant (P < 0.001).

Table 6

HOMA-IR segmented subgroup analysis
	HOMA-IR < 3.72	P for interaction	HOMA-IR > 3.72	P for interaction
	HR (95% CI) P-value	P for interaction	HR (95% CI) P-value	P for interaction
Age dichotomous		0.687		0.525
< 65	0.77 (0.54, 1.11) 0.159		1.05 (1.01, 1.10) 0.019
>= 65	0.86 (0.73, 1.01) 0.071		1.03 (1.00, 1.07) 0.084
Gender		0.486		0.222
Male	0.94 (0.74, 1.21) 0.641		0.99 (0.92, 1.06) 0.731
Female	0.84 (0.71, 1.00) 0.048		1.04 (1.00, 1.07) 0.033
Diabetes		0.934		0.874
Yes	0.87 (0.70, 1.09) 0.234		1.01 (0.98, 1.05) 0.411
No	0.86 (0.71, 1.03) 0.093		1.02 (0.92, 1.13) 0.700
Cancer		0.668		0.768
Yes	0.94 (0.72, 1.21) 0.613		1.03 (0.97, 1.08) 0.334
No	0.86 (0.73, 1.02) 0.077		1.02 (0.99, 1.06) 0.238
Hypoglycemic drugs or insulin users		0.782		0.558
Yes	0.90 (0.70, 1.15) 0.396		1.02 (0.99, 1.06) 0.166
No	0.86 (0.72, 1.02) 0.078		0.99 (0.91, 1.09) 0.906
hazard ratio(HR), 95% confidence interval(CI),and P-value

With the introduction of the phenotype of ‘Metabolic syndrome-associated osteoarthritis’ (MetS-OA) and ‘diabetes-induced-osteoarthritis’(DM-OA)[18–19], researchers have increasingly focused on the link between OA and metabolism in recent years. However, the effect of insulin resistance on the progression or mortality of OA remains controversial [20–21]. This study explores the relationship between different IR surrogates and all-cause mortality in a community-based population with OA in the United States. Using multivariate Cox regression and RCS analysis, we determined that HOMA-IR is a reliable predictor of all-cause mortality risk in patients with OA, displaying a U-shaped association with mortality risk; both high and low levels of HOMA-IR increased the risk.

The baseline characteristics of the study population indicated that participants who died during follow-up were more likely to be older, have a lower education level and income, be more smokers, diabetes, heart failure, stroke, and cancer patients. In segmented multivariate Cox regression analysis, when HOMA-IR was modeled as a continuous variable, the effect values were statistically significant. However, when HOMA-IR was modeled as categorical variables, the effect values were not statistically significant, possibly due to decrease testing efficiency resulting from insufficient sample size for each group.

IR is an important feature of the MetS, and alongside diabetes, obesity, and dyslipidemia, it is also a risk factor for the development of OA and may be associated with adverse outcomes in patients with OA [22]. Aging and obesity have been long established as predominant and possibly preventable risk factors for OA. They not only enhances the load on the weight-bearing joints [23] but also causes misalignment and unfavorable joint mechanics, particularly in the knees, thereby increasing mechanical stress and cartilage degradation leading to OA [24]. However, altered biomechanics do not fully justify the increased risk for OA in non-weight-bearing joints such as the hands and the wrists in obese subjects, pointing to a systemic, non-mechanical influence on the risk for OA [25]. Our study also indicated that OA participants who died did not exhibit higher BMI. Recent studies have drawn a link between OA and MetS, which is characterized by IR. Some studies have found that the presence of IR and OA interact with each other, further complicating the management of OA. A UK Biobank prospective cohort study found that MetS and its components, which include hyperglycemia and dyslipidemia, were associated with an increased risk of OA, particularly in individuals with elevated levels of CRP [26]. The hyperinsulinemic-normal glucose clamp technique is the gold standard for the diagnosis of IR, but is difficult to use in large-scale clinical studies due to its imitations. Therefore, various different IR surrogates, including HOMA-IR, TyG index and TyG-BMI index, are widely used in clinical research.

Previous studies have extensively examined the relationship between IR and OA morbidity, but research on IR and OA outcomes remains limited. For instance, a cross-sectional study involving 3921 patients demonstrated that an increased risk of OA was associated with a higher TyG index, with each incremental unit increase in the TyG index correlating with a 634% higher risk of OA [OR = 7.34; 95% CI: 2.25, 23.93; p = 0.0010] [13]. Additionally, a study from Germany indicated that musculoskeletal impairment and osteoarthritis-related symptoms are linked with insulin resistance [27]. However, our study did not find a significant association between the TyG index and all-cause mortality, possibly due to variations in the follow-up duration. No previous research has explored the correlation between the TyG-BMI index and prognosis in patients with OA, and our findings did not reveal a significant correlation. As for HOMA-IR, our analysis identified it as a robust predictor of all-cause mortality in patients with OA. Specifically, when HOMA-IR was less than 3.72, it was inversely associated with mortality risk; conversely, when HOMA-IR exceeded 3.72, it was positively associated with all-cause mortality, indicating a U-shaped relationship between HOMA-IR levels and mortality in patients with OA. The model evaluation results further supported that HOMA-IR’s strong predictive ability for all-cause mortality. While prior evidence suggests a possible link between OA and diabetes, reinforcing the notion that diabetes might be an important independent risk factor for OA[28], the relationship between IR and OA has not been quantitatively demonstrated through clinical data, nor has the nonlinear relationship been assessed. Our study addresses these gaps by providing new insights into the complex interplay between IR and OA outcomes.

It is important to highlight that immune factors may play a concealed role behind IR substitutes. The metabolic reprogramming of fibroblast-like synoviocytes (FLS) during OA progression is an emerging area of research. Before pathological damage to the articular cartilage is evident, FLS may undergo glucose metabolism reprogramming under the internal environmental disturbances, which is characterized by a shift to inefficient glycolysis despite the presence of sufficient oxygen, a phenomenon also known as Warburg Effect originally identified in tumor cells [29]. This increase glycolysis in FLS leads to the infiltration and polarization of synovial macrophage, significantly elevating the expression of pro-inflammatory mediators such as interleukin (IL)-1β, tumor necrosis factor (TNF)-α, cyclooxygenase (COX)-2, and matrix metalloproteinases (MMP)-3, -13[30]. These pro-inflammatory cytokines not only expand the scope and scale of inflammation, but also synergize to exacerbate the adverse effects in OA including upregulating the expression of proteases, aiding in the degradation of cartilage extracellular matrix (ECM), and ultimately leading to total joint failure [31]. Normally, Insulin could suppress this inflammatory process[32]. However, the abnormal glucose metabolism in FLS and chondrocytes leads to impaired regulation of glucose transporters (GLUT) and a diminished ability to respond to extracellular glucose levels [28,32–34]. Furthermore, adipose tissue (AT) also acts as a significant source of pro-inflammatory cytokines, contributing to low-grade chronic metabolic inflammation [35]. This type of inflammation can exacerbate joint structural damage and is frequently associated with IR and T2DM [36,37].

It is essential to explore the relationship between IR and the mortality of OA. IR contributes to several pathological conditions including endothelial dysfunction, abnormal lipid metabolism, hypertension, and systemic inflammatory, all of which are closely related to the development and adverse outcomes of OA [38]. MMPs can be synthesized and released by the promotion of IR through various pathways [8], which are crucial in the osteogenic transformation and medial artery calcification in vascular smooth muscle cell(VSMC). This process potentially leads to significant cardiovascular events[39,40]. Our study observed that participants who succumbed during follow-up exhibited higher incidences of heart failure and stroke. Furthermore, IR facilitates the production of advanced glycation end products(AGEs), which are significantly involved in the progression of OA and the occurrence of CVD. Accumulation of AGEs leads to increased brittleness of the cartilage, promote matrix stiffness and make the cartilage more sensitive to mechanical stress which result in degradation[41]. AGEs also impair the AMPKα/SIRT1/PGC-1α signaling pathway in chondrocytes, resulting in mitochondrial dysfunction as a result of increased oxidative stress, inflammation, and apoptosis[42]. Additionally, studies indicate that AGEs could upregulate the expression of MMPs in fibroblasts, contributing to myocardial fibrosis[43]. Our study found that the HOMA-IR can serve as an effective predictor of all-cause mortality risk in patients with OA. Both high and low HOMA-IR levels were associated with an increased risk of all-cause mortality, indicating a potential tool for clinicians to assess and manage mortality risk in OA patients proactively.

It is worth noting that HOMA-IR, as one of the IR surrogates, has demonstrated robust predictive capabilities for mortality in diseases related to MetS, including coronary heart disease[44], heart failure[45] and nonalcoholic fatty liver disease [46]. It’s necessary to investigate whether HOMA-IR’s association with all-cause mortality remains consistent across different diseases to broaden its clinical applicability.

The strengths of our study include the out study benefits from a nationally representative sample of the U.S. multiethnic population. Besides, the appropriate NHANES sample weights and covariates were considered while performing the analyses to minimize the impact of confounding variables and improve the generalizability of our findings to the general population.

However, several limitations must be acknowledged. Firstly, the diagnosis of OA in this study was obtained through self-reporting, lacking critical laboratory tests like anti-Cyclic Citrullinated Peptide antibodies and Rheumatoid Factor due to NHANES database restrictions, potentially introducing bias in OA diagnosis and differential diagnosis. Furthermore, NHANES does not collect longitudinal data on key factors such as changes in the metabolic abnormality, which may affect long term outcome, including mortality. Finally, converting HOMA-IR into a categorical variable for analysis and performing segmented stratified analysis may reduce the sample size in each group, thereby decreasing the efficiency of tests.

This study found that HOMA-IR was a reliable predictor of all-cause mortality in patients with OA. HOMA-IR display a U-shaped association with all-cause mortality in patients with OA. Both high or low HOMA-IR were associated with an increase in all-cause mortality.

IR	Insulin resistance
OA	Osteoarthritis
CVD	Cardiovascular diseases
MetS	metabolic syndrome
NHANES	National Health and Nutrition Examination Survey
NDI	National Death Index
NCHS	National Center for Health Statistics
HOMA-IR	Homeostatic model assessment of IR
TyG index	Triglyceride glucose index
TyG-BMI index	Triglyceride glucose-body mass index
ROC	Receiver operating characteristic
AUC	Area under the curve
DCA	Decision Curve Analysis
PIR	Income -poverty ratio
LDL-C	Low density lipoprotein cholesterol
HDL-C	High density lipoprotein cholesterol
TC	Total cholesterol
ALT	Alanine aminotransferase
ALP	Alkaline phosphatase
AST	Aspartate aminotransferase
GGT	Gamma-glutamyltransferase
LDH	Lactate dehydrogenase
HbA1c	Glycosylated hemoglobin
PLT	Platelet
WBC	White blood cell

Acknowledgements

We would like to acknowledge the following financial support: National Natural Science Foundation of China (NO.82374312). We thank the NHANES database for sharing the data.

Author contributions

XMY and ZXG designed the research. XMY, ZPH and XCY collected and analyzed the data. XMY drafted the manuscript. ZPH and ZXG revised the manuscript. All authors contributed to the article and approved the submitted version.

Funding

The funding support from the National Natural Science Foundation of China (NO.82374312) This research received no external funding.

Availability of data and materials

Data can be found at https://www.cdc.gov/nchs/nhanes/.

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors have reviewed and approved the final version of the manuscript.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

Association between different insulin resistance surrogates and all-cause mortality in patients with Osteoarthritis: Evidence from a large population-based study

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