Abstract
For normal‐weight population, the management of prehypertension may be more beneficial by identifying insulin resistance (IR) status than relying solely on traditional indicators of obesity. We investigated the association of triglyceride glucose (TyG) index, a simple surrogate marker of IR, and its combination of obesity indices with prehypertension in lean individuals. A total of 105 070 lean adults without hypertension were included in this analysis. Body mass index (BMI), waist circumference (WC), waist‐to‐height ratio (WtHR), and TyG were calculated according to the corresponding formula; TyG‐BMI, TyG‐WC, and TyG‐WHtR were calculated by multiplying the corresponding two parameters. Gardner‐Altman plots, partial correlation, and logistic regression analyses were applied to explore the associations in continuous variables and quartiles. The prehypertensive ones had higher mean values of TyG, TyG‐BMI, TyG‐WC, and TyG‐WHtR than normotensive individuals. All the four indicators showed positive correlations with systolic blood pressure and diastolic blood pressure. After full adjustment, only TyG‐BMI and TyG‐WC were significantly associated with prehypertension in both genders. Furthermore, TyG‐BMI had the highest OR for prehypertension. Our study showed that TyG‐BMI might be an accessible and complementary monitor in the hierarchical management of non‐obese prehypertensive patients.
Keywords: insulin resistance, non‐obese population, prehypertension, triglyceride glucose (TyG) index
1. INTRODUCTION
Hypertension is a worldwide healthy problem and well‐known factor for cardiovascular disease (CVD). 1 Recent two decades, although within the normal range, a slightly elevated blood pressure (BP) also attracted much attention due to its unfavorable clinical implication. 2 , 3 , 4 So the JNC‐7 announced prehypertension (120‐139/80‐89 mmHg) as a new BP classification criteria in 2003. 5 In 2017, the 2017 ACC/AHA Hypertension Guideline defined 130‐139/80‐89 mmHg as stage 1 hypertension. 6
Although elevated BP is often accompanied by obesity which is often assessed by body mass index (BMI), many individuals with normal BMI are also characterized by elevated BP, especially in East Asian populations. 7 So if some people are prehypertensive and have normal weight, they generally tend to ignore their BP issues, and the primary health care provider also does not know how to manage them. 8 Considering that insulin resistance (IR) is a vital pathological mechanism of elevated BP, 9 recognition of IR in lean prehypertensive individuals may be of substantial clinical importance for the management of prehypertension.
More recently, triglyceride glucose (TyG) index, the product of fasting plasma glucose (FPG), and triglycerides (TG) have been proposed as simple and efficient surrogate marker for early identification of IR. 10 In addition, TyG combined with BMI, waist circumference (WC), and waist‐to‐height ratio (WtHR) have been reported they are more efficient than TyG alone. 11 But no studies have yet been conducted regarding the relationship between these parameters and prehypertension in individuals with normal weight. Thus, this large‐scale cross‐sectional study was designed to investigate the associations of TyG, TyG‐BMI, TyG‐WC, and TyG‐WHtR with prehypertension in lean Chinese adults.
2. METHODS
2.1. Subjects
This study was based on the database of adults who received routine physical examination between January 2013 and July 2019 in the Yangtze River Delta of China. (reviewer #1, comment #2) These people who received routine physical examination were “apparently healthy” and without specific complaints or severe disease, which mean they were either healthy or only suffered from some common chronic diseases such as hypertension, diabetes, fatty liver, dyslipidemia, or hyperuricemia, and thyroid nodules. (reviewer #2, comment #3) (reviewer #2, comment #4).
Considering the calculation of TyG needs FPG and TG, so those who took hypoglycemic agents and lipid‐lowering drugs were excluded. This study focused on patients with prehypertension, so those with hypertension (140/90 mmHg, or self‐report history of hypertension, or current use of antihypertensive medication) were also excluded. Finally, a total of 105 070 lean adults (with BMI 18‐24 kg/m2) without hypertension were analyzed in this study. The study was approved by the ethics committee of Hangzhou Aeronautical Sanatorium of Chinese Air Force.
2.2. Data collection
Anthropometric indicators were measured by well‐trained examiners and in light clothing with no shoes. The systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate (HR) were obtained three times on the right arm after at least 5‐minute rest using automatic BP monitor (HEM‐1000, OMRON, Japan). The blood samples of subjects were collected after a minimum of 8 hour of overnight fasting. Serum levels of FPG, plasma uric acid (UA), total cholesterol (TC), TG, low‐density lipoprotein cholesterol (LDLc), high‐density lipoprotein cholesterol (HDLc), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and gamma‐glutamyl transferase (GGT) were determined by a biochemical autoanalyzer (Advia 1650 Autoanalyzer; Bayer Diagnostics). Abdominal ultrasonography (ATL HDI 5000; Phillips Medical Systems) was undertaken by clinical radiologists using a 3.5 MHz probe for all subjects.
2.3. Definitions
Prehypertension was defined as having either a SBP of 120‐139 mmHg and/or DBP of 80‐89 mmHg. (reviewer #1, comment #8) BMI was calculated as weight divided by the square of height; WC divided by the hip circumference (reviewer #1, comment #9) was WHR; WC divided by height provided the WHtR. TyG = Ln [fasting TG (mg/dL)*FPG (mg/dL)/2] 9 ; TyG‐BMI = TyG × BMI; TyG‐WC = TyG × WC; TyG‐WHtR = TyG × WHtR.
2.4. Statistical analysis
Statistical analysis was performed using SPSS version 18.0 (SPSS Inc) and MedCalc version 19.0 (MedCalc Software). (reviewer #1, comment #10) (reviewer #2, comment #6) Data are expressed as numbers or means ± SD. Categorical variables were compared using the chi‐squared test, and t test was used to test the differences in continuous data. Gardner‐Altman plots were produced using estimation statistics for data visualization. Partial correlation was applied to examine the correlation between BP levels and TyG and its related parameters, which was adjusted for age, biochemical indicators, alcohol intake, smoking status, and non‐alcoholic fatty liver disease.(reviewer #1, comment #2) Logistic regression analyses were applied to explore the associations of TyG and its related parameters with prehypertension. TyG, TyG‐BMI, TyG‐WC, and TyG‐WHtR were divided into four quartiles, and the lowest quartile was used as a reference in regression analysis. Adjustment conditions were the same as partial correlation analysis. Receiver operating characteristic (ROC) analyses and the area under ROC curves (AUC) were used to evaluate the ability of these indicators to distinguish prehypertension. AUCs of different indicators were compared by DeLong method in MedCalc. (reviewer #2, comment #6) P‐value < .05 was considered statistically significant.
3. RESULTS
The mean age of 105 070 lean adults without hypertension was 42.6 years and 54.7% were women. The overall proportion of prehypertension was 39.7%, (reviewer #1, comment #4) and 29.5% in lean women and 52.1% in lean men. The clinical characteristics of lean individuals without hypertension are shown in Table 1. Compared to normotensive individuals, the prehypertensive ones were older, slight fatter, with a higher levels of HR and liver enzymes, and less favorable metabolic profile. (reviewer #1, comment #11) (reviewer #1, comment #13) In addition, prehypertensive ones had higher mean values of TyG, TyG‐BMI, TyG‐WC, and TyG‐WHtR in both genders (Figure 1).
Table 1.
The clinical characteristics of lean individuals without hypertension
| Characteristics | Women | Men | ||||
|---|---|---|---|---|---|---|
| Normotension | Prehypertension | P value | Normotension | Prehypertension | P value | |
| No., n | 40 500 | 16 968 | ‐ | 22 818 | 24 784 | ‐ |
| Age, y | 39.5 ± 10.2 | 45.3 ± 13.0 | <.001 | 40.5 ± 11.6 | 42.0 ± 13.5 | <.001 |
| BMI (kg/m2) | 20.7 ± 1.8 | 21.3 ± 1.8 | <.001 | 21.5 ± 1.8 | 21.9 ± 1.6 | <.001 |
| WC (cm) | 68.9 ± 5.6 | 70.8 ± 5.9 | <.001 | 76.2 ± 6.0 | 77.3 ± 5.9 | <.001 |
| WHR | 0.77 ± 0.05 | 0.79 ± 0.06 | <.001 | 0.83 ± 0.05 | 0.84 ± 0.05 | <.001 |
| WHtR | 0.43 ± 0.04 | 0.44 ± 0.04 | <.001 | 0.44 ± 0.04 | 0.45 ± 0.03 | <.001 |
| SBP (mmHg) | 105.9 ± 8.2 | 126.1 ± 6.4 | <.001 | 109.4 ± 7.1 | 126.9 ± 6.3 | <.001 |
| DBP (mm Hg) | 65.0 ± 6.9 | 75.8 ± 7.5 | <.001 | 68.0 ± 6.2 | 77.6 ± 6.7 | <.001 |
| Heart rate (beats/min) | 81.0 ± 11.4 | 84.5 ± 13.7 | <.001 | 76.2 ± 11.6 | 80.4 ± 12.9 | <.001 |
| FPG (mmol/L) | 5.28 ± 0.54 | 5.50 ± 0.80 | <.001 | 5.45 ± 0.86 | 5.62 ± 1.02 | <.001 |
| TC (mmol/L) | 4.56 ± 0.84 | 4.82 ± 0.91 | <.001 | 4.58 ± 0.83 | 4.68 ± 0.86 | <.001 |
| TG (mmol/L) | 0.96 ± 0.58 | 1.13 ± 0.73 | <.001 | 1.29 ± 0.89 | 1.41 ± 1.03 | <.001 |
| HDLc (mmol/L) | 1.71 ± 0.34 | 1.71 ± 0.36 | .655 | 1.47 ± 0.31 | 1.48 ± 0.32 | <.001 |
| LDLc (mmol/L) | 2.36 ± 0.69 | 2.56 ± 0.75 | <.001 | 2.51 ± 0.70 | 2.57 ± 0.72 | <.001 |
| UA (μmol/L) | 263.0 ± 52.3 | 269.5 ± 56.3 | <.001 | 362.1 ± 68.4 | 365.3 ± 70.9 | <.001 |
| ALT (U/L) | 17.3 ± 12.2 | 18.7 ± 17.8 | <.001 | 24.6 ± 18.6 | 26.1 ± 24.1 | <.001 |
| AST (U/L) | 18.5 ± 7.3 | 19.4 ± 9.5 | <.001 | 21.0 ± 10.8 | 21.7 ± 11.7 | <.001 |
| ALP (U/L) | 56.4 ± 16.2 | 61.7 ± 18.5 | <.001 | 67.1 ± 17.3 | 68.3 ± 17.1 | <.001 |
| GGT (U/L) | 17.0 ± 14.0 | 19.2 ± 17.1 | <.001 | 29.5 ± 29.3 | 34.0 ± 36.5 | <.001 |
Abbreviations: ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; DBP, diastolic blood pressure; FPG, fasting plasma glucose; GGT, gamma‐glutamyl transpeptidase. HDLc, high‐density lipoprotein cholesterol; LDLc, low‐density lipoprotein cholesterol; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; UA, plasma uric acid; WC, waist circumference; WHR, waist‐to‐hip ratio; WHtR, waist‐to‐height ratio.
Figure 1.
Gardner‐Altman plots for TyG, TyG‐BMI, TyG‐WC, and TyG‐WHtR according blood pressure status. The raw data of TyG and its related parameters were shown on the left axis, and the mean difference between the two groups was depicted as a red dotted line and was shown on the right; BMI, body mass index; TyG, triglyceride and glucose index; WC, waist circumference; WHtR, waist‐to‐height ratio
After controlling for confounding factors, TyG, TyG‐BMI, TyG‐WC, and TyG‐WHtR were significantly correlated with each other (Table S1) and BP levels in both genders (Table 2). (reviewer #1, comment #7) The SBP and DBP levels were significantly elevated from the lowest to top quartiles of TyG, TyG‐BMI, TyG‐WC, and TyG‐WHtR. Similarly, the proportion of prehypertension showed a significant increase trend as ascending quartiles of TyG, TyG‐BMI, TyG‐WC, and TyG‐WHtR in both genders (Table 3).
Table 2.
Partial correlations coefficients between blood pressure level and TyG and its related parameters
| Variable | SBP | DBP | ||
|---|---|---|---|---|
| r | P values | r | P values | |
| TyG | ||||
| Women | 0.121 | <.001 | 0.128 | <.001 |
| Men | 0.068 | <.001 | 0.135 | <.001 |
| TyG‐BMI | ||||
| Women | 0.144 | <.001 | 0.122 | <.001 |
| Men | 0.115 | <.001 | 0.150 | <.001 |
| TyG‐WC | ||||
| Women | 0.141 | <.001 | 0.116 | <.001 |
| Men | 0.100 | <.001 | 0.141 | <.001 |
| TyG‐WHtR | ||||
| Women | 0.147 | <.001 | 0.111 | <.001 |
| Men | 0.106 | <.001 | 0.141 | <.001 |
All adjusted for age, biochemical indicators, alcohol intake, smoking status, and non‐alcoholic fatty liver disease.
Abbreviations: BMI, body mass index; DBP, diastolic blood pressure; SBP, systolic blood pressure; TyG, triglyceride and glucose index; WC, waist circumference; WHtR, waist‐to‐height ratio.
Table 3.
The change of blood pressure level and the proportion of prehypertension by quartiles of TyG and its related parameters
| Variable | Women | Men | ||||||
|---|---|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q1 | Q2 | Q3 | Q4 | |
| TyG | ||||||||
| SBP (mmHg) | 110.6 ± 12.9 | 112.4 ± 13.9 | 115.0 ± 15.3 | 121.0 ± 17.8 | 120.8 ± 14.5 | 121.7 ± 15.0 | 123.3 ± 15.3 | 126.2 ± 16.3 |
| DBP (mm Hg) | 67.3 ± 9.2 | 68.6 ± 9.5 | 70.0 ± 10.0 | 72.7 ± 10.7 | 73.2 ± 9.8 | 74.5 ± 9.9 | 76.2 ± 10.2 | 78.7 ± 10.6 |
| Prehypertension (%) | 21.7 | 25.4 | 30.8 | 41.8 | 47.0 | 48.8 | 53.2 | 57.6 |
| TyG‐BMI | ||||||||
| SBP (mmHg) | 109.8 ± 13.0 | 112.2 ± 13.8 | 115.5 ± 15.3 | 121.5 ± 17.3 | 119.3 ± 14.6 | 122.3 ± 15.1 | 123.6 ± 15.1 | 126.7 ± 16.0 |
| DBP (mm Hg) | 67.4 ± 9.1 | 68.4 ± 9.5 | 69.9 ± 10.1 | 72.9 ± 10.7 | 72.7 ± 9.5 | 74.7 ± 10.1 | 76.2 ± 10.1 | 78.9 ± 10.6 |
| Prehypertension (%) | 20.3 | 25.1 | 31.5 | 43.0 | 43.7 | 49.9 | 54.0 | 59.6 |
| TyG‐WC | ||||||||
| SBP (mmHg) | 109.8 ± 12.5 | 111.9 ± 13.7 | 115.0 ± 15.3 | 121.7 ± 17.6 | 119.6 ± 14.4 | 121.7 ± 14.9 | 123.5 ± 15.2 | 126.5 ± 16.0 |
| DBP (mm Hg) | 67.3 ± 9.0 | 68.4 ± 9.5 | 69.8 ± 10.1 | 72.8 ± 10.7 | 72.6 ± 9.5 | 74.5 ± 10.0 | 76.2 ± 10.1 | 78.7 ± 10.4 |
| Prehypertension (%) | 20.7 | 24.6 | 30.4 | 43.3 | 44.4 | 49.5 | 53.6 | 58.8 |
| TyG‐WHtR | ||||||||
| SBP (mmHg) | 109.8 ± 12.2 | 111.7 ± 13.5 | 114.8 ± 15.1 | 122.1 ± 18.0 | 119.4 ± 14.0 | 121.6 ± 14.6 | 123.3 ± 15.2 | 127.1 ± 16.5 |
| DBP (mm Hg) | 67.4 ± 8.9 | 68.3 ± 9.4 | 69.7 ± 10.0 | 72.8 ± 10.8 | 72.5 ± 9.4 | 74.4 ± 9.8 | 76.2 ± 10.1 | 78.9 ± 10.6 |
| Prehypertension (%) | 20.7 | 24.8 | 30.0 | 43.7 | 45.0 | 49.5 | 53.0 | 59.0 |
Compared to the previous quartile, the mean value of BP and proportion of prehypertension were increased significantly, and all P < .001.
Abbreviations: BMI, body mass index; DBP, diastolic blood pressure; Q, quartile; SBP, systolic blood pressure; TyG, triglyceride and glucose index; WC, waist circumference; WHtR, waist‐to‐height ratio.
Results of logistic regression analysis are shown in Figure 2. After full adjustment, TyG‐BMI and TyG‐WC were significantly associated with prehypertension in both genders, whereas TyG was significantly associated with prehypertension only in women and TyG‐WHtR was not significantly associated with prehypertension in both genders. In women, the OR for prehypertension in the highest quartile of TyG, TyG‐BMI, TyG‐WC were 1.299 (95% CI 1.185‐1.423), 1.628 (95% CI 1.455‐1.821), and 1.379 (95% CI 1.180‐1.611), respectively; In men, the OR for prehypertension in the highest quartile of TyG‐BMI and TyG‐WC were 1.669 (95% CI 1.482‐1.881), 1.355 (95% CI 1.169‐1.571), respectively.
Figure 2.
Logistic regression analysis for the associations of TyG, TyG‐BMI, TyG‐WC, and TyG‐WHtR with prehypertension. The first quartile of TyG and its related parameters was used as a reference; BMI, body mass index; Q, quartile; TyG, triglyceride and glucose index; WC, waist circumference; WHtR, waist‐to‐height ratio
The ROC curves of traditional obesity indicators, (reviewer #2, comment #2) TyG, and its related parameters for prehypertension are shown in Figure S1. The AUC value of TyG, TyG‐BMI, TyG‐WC, and TyG‐WHtR to distinguish prehypertension are summarized in Table 4. Among women, no significant difference in the AUC of TyG‐BMI [0.619 (95% CI 0.614‐0.625)], TyG‐WC [0.618 (95% CI 0.612‐0.623)], and TyG‐WHtR [0.619 (95% CI 0.613‐0.624)], which were significantly higher than that of TyG [0.605 (95% CI 0.600‐0.611)]. Among men, TyG‐BMI had the largest AUC for prehypertension [0.570 (95% CI 0.564‐0.575)]. The results of pairwise comparisons between indicators are shown in Table S2. (reviewer #2, comment #6).
Table 4.
The AUC with its 95% CI for distinguishing prehypertension by obesity indicators and combination with TyG
| Variable | Women | Men | ||||
|---|---|---|---|---|---|---|
| AUC | 95% CI | P values | AUC | 95% CI | P values | |
| BMI | 0.592 | 0.587‐0.598 | <.001 | 0.562 | 0.556‐0.567 | <.001 |
| WC | 0.595 | 0.589‐0.600 | <.001 | 0.556 | 0.551‐0.562 | <.001 |
| WHtR | 0.599 | 0.594‐0.605 | <.001 | 0.553 | 0.547‐0.558 | <.001 |
| TyG | 0.605 | 0.600‐0.611 | <.001 | 0.550 | 0.544‐0.555 | <.001 |
| TyG‐BMI | 0.619 | 0.614‐0.625 | <.001 | 0.570 | 0.564‐0.575 | <.001 |
| TyG‐WC | 0.618 | 0.612‐0.623 | <.001 | 0.565 | 0.559‐0.570 | <.001 |
| TyG‐WHtR | 0.619 | 0.613‐0.624 | <.001 | 0.562 | 0.556‐0.567 | <.001 |
Abbreviations: AUC, area under the receiver operating characteristic curve; BMI, body mass index; CI, confidence interval; TyG, triglyceride and glucose index; WC, waist circumference; WHtR, waist‐to‐height ratio.
4. DISCUSSION
In this large‐scale cross‐sectional study, we investigated the association of TyG and its combination of obesity indices with prehypertension in lean Chinese adults. Our data revealed that, although in normal‐weight individuals, the combination of adiposity status and TyG (TyG‐BMI and TyG‐WC) still showed a strong and positive association with prehypertension in both genders. Moreover, TyG‐BMI outperformed other parameters with a higher OR and larger AUC. The results suggested that TyG‐BMI might be an accessible and effective assessment indicator in the hierarchical management of lean prehypertensive individuals.
Elevated BP is the most common comorbidity of obesity. 12 Nevertheless, there is also a certain proportion of prehypertension in individuals with normal BMI, especially in Asian populations. 13 Most studies on elevated BP in lean people have focused on populations in low‐ to middle‐income countries. 14 In the present study, unlike previous research, our research subjects are the population in the Yangtze River Delta region, where is one of the most developed regions in China. This study may provide some information for the prevention and treatment of hypertension in lean populations in high‐ and middle‐income areas. (reviewer #1, comment #2).
Prehypertension is a common condition that affects 25%‐50% of adults worldwide. 15 In this study, the overall proportion of prehypertension among the lean subjects was 39.7% which was higher than the reported proportion of China and other East Asian populations that were not grouped by BMI (31%‐37%). 16 , 17 The main reason was that we excluded hypertensive and overweight/obese individuals which made the base of the study population less. (reviewer #1, comment #4).
Another interesting result of this study was that the proportion of prehypertension in lean men was significantly higher than that in lean women (52.1% vs 29.5%). Beyond the disparity in gender itself, another possible explanation for this relatively large difference may be the source of the study population. In China, the routine physical examination is not covered by government health insurance, and individuals need to pay for physical examinations themselves. However, many enterprises and institutions organize routine physical examinations for their employees every year, which is an employee benefit. So most of the subjects in this study were occupational population. Our previous study suggested that these men had worse metabolic conditions, which may be related to their occupational stress and unhealthy lifestyles. 18 (reviewer #1, comment #4).
For the management of prehypertension, weight management based on dietary and exercise interventions intervention is still the most important method, 19 even in normal‐weight population. Several studies and meta‐analysis have demonstrated that the risk of hypertension associated with adiposity is greater in lean than in not‐lean subjects. 14 , 20 Being as lean as possible within the normal BMI range may be the best suggestion to prevent hypertension. 21 , 22 In this study, the lean prehypertensive ones were indeed slight fatter than lean normotensive individuals. However, the difference between these traditional obesity indicators was too small to grasp in clinical practice. So some alternative clinical indicators are needed to help stratify the lean prehypertensive individuals and play a monitoring role in the management.
Given that IR is the core pathological mechanism of chronic metabolic disease, 9 identification of IR may help to stratify lean individuals with prehypertension and develop targeted management strategies. Nevertheless, estimating IR remains a challenge in clinical practice. The hyperinsulinemic‐euglycemic clamp (HEC), the gold standard for assessing IR, is a time‐ and resource‐consuming tool and unsuitable for routine clinical practice. 23 Although homeostasis model assessment of IR (HOMA‐IR) has a wide range of clinical applications, the relative high cost and low repeatability of measuring plasma insulin also limits the application of HOMA‐IR. 24 In clinical work, the accuracy of insulin measurement is easily affected by the choice of kits, calibration set‐up in kits, and conversions between units. One study showed that the value of HOMA2‐IR calculated by 11 insulin kits varied by up to twofold. 25 (reviewer #1, comment #6).
Unlike HEC and HOMA‐IR, TyG does not require insulin but only FPG and TG (the two most commonly used clinical indicators), which not only reduces costs but also improves the stability of the IR evaluation. 26 , 27 (reviewer #1, comment #6) Subsequent studies confirmed that TyG closely related to type 2 diabetes, prehypertension, hypertension, hyperuricemia, and CVD. 28 Nevertheless, the research on TyG in lean populations is relatively rare. A recent study showed that TyG was associated with carotid atherosclerosis and arterial stiffness mainly in lean postmenopausal women, 29 which was similar to our findings that TyG was associated with prehypertension only in lean women. The reason for the gender disparity of the performance of TyG is not clear but it may have something to do with the gender‐specific differences in glycolipid metabolism, IR, and elevated BP. 30 , 31
Attributing the well‐validated role of obesity in IR, the integration of TyG and adiposity parameters theoretically has an advantage to reflect IR. Lim et al and Er et al concluded that TyG‐BMI was superior to TyG, TyG‐WC, and TyG‐WHtR for IR prediction. 32 , 33 Another study also demonstrated that TyG‐BMI had higher OR and AUC values for prediabetes in Colombian men. 34 But what about such superiority among normal‐weight individuals? In the present study, both TyG‐BMI and TyG‐WC performed better than TyG along in both genders. An implication of this is the possibility that combined use of obesity indicators may increase the effectiveness of IR assessments even in lean populations.
It is somewhat surprising that TyG‐BMI performed best but TyG‐WHtR performed worst among these indicators after we had excluded those individuals with elevated BMI. Generally, WHtR, a marker of central adiposity, may be superior to BMI in detecting risk factors of CVD, particularly in Asians. 35 Nevertheless, some other studies have not shown the superiority of WHtR over BMI for identifying cardiometabolic risk factors. 36 , 37 These inconsistent results are not only related to the differences in age, ethnicity, and gender, but may also be related to the limited ability of WHtR to differentiate abdominal subcutaneous and visceral fat. Therefore, we still cannot conclude which obesity indicator is the best out of a specific context, and further additional studies on TyG combined anthropometric indicators are required. (reviewer #1, comment #5).
The main strength of the present study is a relatively large sample size. But we must also mention the limitations of this study. First, the cross‐sectional design cannot show a causal relationship between prehypertension and TyG and its related parameters in lean individuals. Second, this might be the first large‐scale study exploring the association of TyG and its related parameters with prehypertension in lean subjects to the best of our knowledge. So rare related studies may cause a limited possible comparisons. Third, subjects of this study might limit the generalizability of the results to other ethnic groups.
In conclusion, TyG‐BMI, incorporating TyG and obesity indicators, has the potential to become a cost‐effective and complementary monitor in the hierarchical management of lean prehypertensive individuals. However, further prospective and randomized studies will be required to confirm our findings.
CONFLICT OF INTEREST
No conflicts of interest to disclose.
AUTHOR CONTRIBUTION
All authors were involved in developing the study concept and design, data acquisition, data management, and interpretation of results. XZL established the database. ZYZ and SXL undertook the statistical analysis of the data and wrote the manuscript. HX and XX helped with statistical analysis. XZL and XXZ involved in designing, editing, and review. All authors have approved the final version of this submission.
Supporting information
Fig S1
Table S1‐S2
Zeng ZY, Liu SX, Xu H, Xu X, Liu XZ, Zhao XX. Association of triglyceride glucose index and its combination of obesity indices with prehypertension in lean individuals: A cross‐sectional study of Chinese adults. J Clin Hypertens. 2020;22:1025–1032. 10.1111/jch.13878
Zhen Yu Zeng, Su Xuan Liu, Hao Xu contributed equally to this work.
Funding information
This work was supported by National Natural Science Foundation of China (81570208).
Contributor Information
Xing Zhen Liu, Email: xzliu7@163.com.
Xian Xian Zhao, Email: 13601713431@163.com.
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Supplementary Materials
Fig S1
Table S1‐S2