Abstract
BACKGROUND:
Insulin resistance may be present in healthy adults and is associated with poor health outcomes. Obesity is a risk factor for insulin resistance, but most obese adults do not have insulin resistance. Fitness may be protective, but the association between fitness, weight, and insulin resistance has not been studied in a large population of healthy adults.
METHODS:
A cross-sectional analysis of cardiorespiratory fitness, body mass index, and markers of insulin resistance was performed. Study participants were enrolled at the Cooper Clinic in Dallas, Texas. The analysis included 19,263 women and 48,433 men with no history of diabetes or cardiovascular disease. Cardiorespiratory fitness was measured using exercise treadmill testing. Impaired fasting glucose (100–125 mg/dL) and elevated fasting triglycerides (≥150 mg/dL) were used as a markers of insulin resistance.
RESULTS:
Among individuals with normal weight, poor fitness was associated with 2.2-fold higher odds of insulin resistance in women (1.4–3.6; P = .001) and 2.8-fold higher odds in men (2.1–3.6; P <.001). The impact of fitness remained significant for overweight and obese individuals, with the highest risk group being the unfit obese. Among obese women, the odds ratio for insulin resistance was 11.0 for fit women (8.7–13.9; P <.001) and 20.3 for unfit women (15.5–26.5; P <.001). Among obese men, the odds ratio for insulin resistance was 7.4 for fit men (6.7–8.2; P < .001) and 12.9 for unfit men (11.4–14.6; P < .001).
CONCLUSIONS:
Independent of weight, poor fitness is associated with risk of insulin resistance. Obese individuals, particularly women, may benefit from the greatest absolute risk reduction by achieving moderate fitness.
Keywords: Fitness, Insulin resistance, Lipids, Obesity
INTRODUCTION
Insulin resistance is an independent risk factor for cardiovascular disease,1,2 and laboratory work has provided evidence that insulin resistance plays a causal role in atherosclerosis.3 Notably, insulin resistance may be present in otherwise healthy non-diabetic individuals, placing these individuals at risk for poor health outcomes.4 The causes of insulin resistance are an area of active research. The association between increased weight and insulin resistance is well established, but both experimental and observational data suggest that the majority of non-diabetic obese adults do not have insulin resistance,5–7 and perhaps 10% of normal weight adults do have insulin resistance.5,8 Fitness may be an important modifying factor. Small human studies have shown that physical activity is associated with improved insulin sensitivity,9,10 and physical inactivity is associated with increased insulin resistance.11 However, large-scale studies are lacking.
Studying insulin resistance is challenging because its direct quantification requires measuring insulin-mediated glucose disposal, which is costly and time consuming.12 Thus, most human studies of insulin resistance have used surrogate measurements, such as fasting insulin or insulin response to a glucose challenge. Although these surrogates have been crucial for experimental work, they are not universally available in normal clinical settings, which is a barrier to large-scale observational studies. Alternatively, fasting glucose is readily available, and large studies have demonstrated an association between poor fitness and impaired fasting glucose in men13 and between poor fitness and diabetes in both men13 and women.14
Impaired fasting glucose is defined as fasting glucose ≥100 mg/dL (5.6 mmol/L) and <126 mg/dL (7 mmol/L).15 Although impaired fasting glucose is a strong risk factor for the development of diabetes, impaired fasting glucose alone is a poor marker of insulin resistance.16 Further, the link between impaired fasting glucose and cardiovascular events, in the absence of insulin resistance, is controversial.3,17 The combined presence of impaired fasting glucose and elevated triglycerides, defined as ≥150 mg/dL (1.7 mmol/L), serves as a better predictor of insulin resistance than impaired fasting glucose alone.16 Moreover, these combined measurements are predictive of atherosclerosis in otherwise healthy individuals.18
To better understand the relationship between physical fitness and insulin resistance, we conducted a cross-sectional study of cardiorespiratory fitness, body mass index (BMI), and the combination of impaired fasting glucose and elevated triglycerides as simple markers of insulin resistance. We hypothesized that poor cardiorespiratory fitness would be associated with insulin resistance as defined by these two markers and that this increased risk would be significant in both genders across weight categories.
METHODS
Study Participants and Measurements
Participants who presented to the Cooper Clinic in Dallas, Texas, from 1978 to 2019 for a preventive medical assessment were considered for inclusion in this analysis. Informed consent was obtained from all participants. A clinical evaluation was performed as previously described.19 Briefly, it included a personal medical history; measurements of resting vital signs, height, and weight; and an examination by a physician. Fasting blood chemistries and an electrocardiogram were obtained, and participants underwent an exercise treadmill test using the modified Balke protocol.20
Participants were excluded if they had a history of myocardial infarction, stroke, other cardiovascular disease, diabetes, or a baseline measurement of fasting glucose >125 mg/dL. They were also excluded if they were taking either niacin or fibrates. Body mass index was calculated as weight in kilograms divided by height in meters squared. Normal weight was defined as a BMI of 18.5–24.9 kg/m2, overweight as 25–29.9 kg/m2, and obese as ≥30 kg/m2. Cardiorespiratory fitness was quantified by measuring the duration of maximal exercise during the treadmill test. The maximal metabolic equivalent level for each participant was computed based on the final treadmill speed and grade.21 Participants were classified as unfit if their fitness level was in the lowest of 5 age- and gender-specific categories as previously described.22 Otherwise, the participant was classified as fit.
Statistical Analysis
Baseline characteristics of the study cohort were summarized by gender and insulin resistance category. Insulin resistance categories were defined based on the markers of fasting glucose and fasting triglycerides. The 4 possible categories were 1) normal fasting glucose (<126 mg/dL) and triglycerides < 150 mg/dL; 2) normal fasting glucose and triglycerides ≥150 mg/dL; 3) impaired fasting glucose (≥100 mg/dL and <126 mg/dL) and triglycerides <150 mg/dL; and 4) impaired fasting glucose and triglycerides ≥150 mg/dL. Differences across insulin resistance categories by gender were tested using likelihood-ratio chisquare statistics for nominal characteristics and Kruskal-Wallis statistics for continuous characteristics. Multiple logistic regression was used to estimate insulin resistance odds ratios for BMI and fitness category by gender, adjusted for age, current smoking and LDL cholesterol. All analyses were conducted with SAS/STAT version 9.4 software (SAS Institute Inc., Cary NC, USA).
RESULTS
The analytic cohort included 19,263 women and 48,433 men. Table 1 summarizes the demographic and cardiometabolic profiles of the cohort, stratified by gender. On average, women tended to have a less adverse cardiometabolic profile, with lower BMI, lower blood pressure, lower fasting glucose, lower low-density lipoprotein cholesterol (LDL-C), lower triglycerides, and higher high-density lipoprotein cholesterol (HDL-C). Women also had a lower prevalence of smoking.
Table 1.
Baseline Characteristics of 19,263 Women and 48,433 Men Included in the Study Cohort of the Cooper Center Longitudinal Study from 1971 to 2019
| Women (n = 19,263) | Men (n =48,433) | |
|---|---|---|
| Age | 48.1 (8.5) | 47.1 (8.3) |
| Body mass index (kg/m2) | 24.2 (4.4) | 27.1 (3.9) |
| Normal weight* | 13,022 (67.6) | 15,411 (31.8) |
| Overweight* | 4357 (22.6) | 24106 (49.8) |
| Obese* | 1884 (9.8) | 8916 (18.4) |
| Cardiorespiratory fitness (MET) | 9.5 (2.0) | 11.6 (2.3) |
| Fit* | 17,528 (91) | 42,818 (88.4) |
| Unfit* | 1735 (9.0) | 5615 (11.6) |
| Systolic blood pressure (mmHg) | 114.1 (14.6) | 121.6 (13.2) |
| Diastolic blood pressure (mmHg) | 76.7 (9.4) | 81.7 (9.4) |
| Fasting glucose (mg/dL) | 92.5 (8.5) | 97.8 (8.8) |
| Total cholesterol (mg/dL) | 201.1 (37.1) | 204(38.5) |
| HDL-C (mg/dL) | 65.3 (16.4) | 47.6 (12.4) |
| Triglycerides (mg/dL) | 94.1 (51.5) | 126.2 (68) |
| LDL-C (mg/dL) | 117 (33.6) | 131.4 (35.2) |
| Current smoker* | 1228 (6.4) | 7266 (15.0) |
HDL-C = high-density lipoprotein cholesterol, LDL-C = low-density lipoprotein cholesterol, MET = metabolic equivalent.
Categorical variable. Data is presented as number (percentage). For all other variables data is presented as mean (standard deviation).
Self-reported ethnicity was available for 13,908 women (72%) and 29,782 men (61%). Among the women with available data, 12,799 (92%) reported as non-Hispanic white, 469 (3%) reported as Hispanic, 291 (2%) reported as African American, 229 (2%) reported as Asian, and 120 (1%) reported as other. Among the men with available data, 27,862 (94%) reported as non-Hispanic white, 750 (3%) reported as Hispanic, 489 (2%) reported as African American, 430 (1%) reported as Asian, and 251 (<1%) reported as other.
Table 2 shows the demographic and cardiometabolic traits of the cohort when stratified by the presence of markers of insulin resistance. For both women and men, markers of insulin resistance were associated with older age, higher blood pressure, increased BMI, and lower fitness. The markers of insulin resistance were also associated with higher prevalence of smoking.
Table 2.
Characteristics of the Women and Men in the Study Cohort, Stratified by the Presence of Markers of Insulin Resistance
| Women | Normal Fasting Glucose, Triglycerides <150 mg/dL (n = 13,991) | Normal Fasting Glucose, Triglycerides ≥150 mg/dL (n = 1612) | Impaired Fasting Glucose, Triglycerides <150 mg/dL (n = 2878) | Impaired Fasting Glucose, Triglycerides ≥150 mg/dL (n = 782) |
|---|---|---|---|---|
| Age | 47.1 (8.2) | 50.7 (8.7) | 50.4 (8.9) | 53.3 (8.4) |
| Body mass index (kg/m2) | 23.5 (3.9) | 26.4 (4.8) | 25.1 (4.8) | 28.6 (4.4) |
| Normal weight* | 10,358 (74) | 726(45) | 1722 (59.8) | 216 (27.6) |
| Overweight* | 2728 (19.5) | 569 (35.3) | 760 (26.4) | 300 (38.4) |
| Obese* | 905 (6.5) | 317 (l9.7) | 396 (13.8) | 266 (34) |
| Cardiorespiratory fitness (MET) | 9.8 (2.0) | 8.5 (1.6) | 9.1 (2.0) | 7.9 (1.6) |
| Fit* | 13,019 (93.1) | 1365 (84.7) | 2550 (88.6) | 594(76) |
| Unfit* | 972 (6.9) | 247 (15.3) | 328 (11.4) | 188 (24) |
| Systolic blood pressure (mmHg) | 112.2 (13.8) | 119.1 (15.5) | 117.6 (15.2) | 123.8 (15.4) |
| Diastolic blood pressure (mmHg) | 75.8 (9.2) | 79.7 (9.4) | 78.3 (9.7) | 81.5 (9.4) |
| Fasting glucose (mg/dL) | 89.3 (5.9) | 90.9 (5.6) | 105.0 (4.9) | 107.3 (6.1) |
| Total cholesterol (mg/dL) | 195.9 (34.5) | 224.5 (40.1) | 206.1 (37.2) | 228.4 (41.0) |
| HDL-C (mg/dL) | 67.1 (16.2) | 56.6 (14.9) | 64.7 (16.0) | 52.6 (13.4) |
| Triglycerides (mg/dL) | 77.4 (28.5) | 197.5 (47.4) | 86.8 (29.8) | 207.0 (54.0) |
| LDL-C (mg/dL) | 113.3 (31.5) | 128.5 (39.1) | 124.0 (34.7) | 134.4 (39.1) |
| Current smoker* | 840 (6.0) | 130 (8.1) | 187 (6.5) | 71 (9.1) |
| Men | Normal Fasting Glucose, Triglyceride <150 mg/dL (n = 22 | Normal Fasting s Glucose, Triglycerides ,484) ≥150 mg/dL (n = 689^ | Impaired Fasting Glucose, Triglycerides i) <150 mg/dL (n = 12,50 | Impaired Fasting Glucose, Triglycerides 8) ≥150 mg/dL (n = 6547) |
| Age | 46.4 (8.2) | 46.1 (7.6) | 48.5 (8.6) | 48.2 (7.9) |
| Body mass index (kg/m2) | 26.2 (3.5) | 28.1 (3.8) | 27.0 (3.9) | 29.1 (4.2) |
| Normal weight* | 9213 (41.0) | 1299 (18.8) | 4029 (32.2) | 870 (13.3) |
| Overweight* | 10,584 (47.1) | 3844 (55.8) | 6259 (50.0) | 3419 (52.2) |
| Obese* | 2687 (12.0) | 1751 (25.4) | 2220 (17.7) | 2258 (34.5) |
| Cardiorespiratory fitness (MET) | 12.2 (2.3) | 10.9 (1.9) | 11.6 (2.4) | 10.4 (1.9) |
| Fit* | 20,804 (92.5) | 5680 (82.4) | 11,228 (89.9) | 5106 (78.0) |
| Unfit* | 1680 (7.5) | 1214 (17.6) | 1280 (10.2) | 1441 (22.0) |
| Systolic blood pressure (mmHg) | 119.5 (12.5) | 122.4 (12.9) | 123.0 (13.6) | 125.4(13.6) |
| Diastolic blood pressure (mmHg) | 80.2(9.1) | 83.2 (9.3) | 82.0 (9.5) | 84.7 (9.5) |
| Fasting glucose (mg/dL) | 91.9 (5.2) | 92.9 (4.9) | 106.1 (5.5) | 107.1 (5.9) |
| Total cholesterol (mg/dL) | 195.6 (35.2) | 219.1 (39.1) | 201.3 (37.2) | 224.0 (39.9) |
| HDL-C (mg/dL) | 50.6 (12.3) | 40.6 (9.5) | 50.1 (12.3) | 40.3 (9.0) |
| Triglvcerides (mg/dL) | 89.8 (29.4) | 211.7 (56.0) | 95.8 (29.1) | 219.1 (60.5) |
| LDL-C (mg/dL) | 127.1 (33.3) | 136.2 (36.9) | 132.1 (34.9) | 139.9 (37.8) |
| Current smoker* | 2936 (13.1) | 1321 (19.2) | 1745 (14.0) | 1264 (19.3) |
HDL-C = high-density lipoprotein cholesterol, LDL-C = low-density lipoprotein cholesterol, MET = metabolic equivalent.
Categorical variable. Data is presented as number (percentage). For all other variables data is presented as mean (standard deviation).
Of the 13,022 normal weight women, 216 (2%) had both markers of insulin resistance. Of the 15,411 normal weight men, 870 (6%) had both markers of insulin resistance. Conversely, 905 of the 1,884 obese women (48%) and 2,687 of the 8916 obese men (30%) had neither marker (Tables 1 and 2).
The associations between BMI, cardiorespiratory fitness, and the markers of insulin resistance were estimated using multiple logistic regression, adjusted for age, LDL-C, and smoking status. The odds of having both markers of insulin resistance versus neither marker of insulin resistance in each weight and fitness category are shown in the Figure. Both increased weight and poor fitness were associated with higher odds of insulin resistance compared with the reference group of fit normal-weight individuals. Among normal weight, apparently healthy individuals, poor fitness was associated with 2.2-fold higher odds of insulin resistance in women (1.4–3.6; P = 0.001) and 2.8-fold higher odds in men (2.1–3.6; P <.001). In the overweight and obese categories, women showed a trend toward higher odds ratios than men. Women who were obese and unfit had the highest odds ratio of any group, at 20.3 (15.5–26.5; P <.001). Men who were obese and unfit had an odds ratio of 12.9 (11.4–14.6; P <.001). Reflecting the elevated risk associated with increased BMI, the absolute difference in odds ratios tended to be larger at higher weight categories (Figure). Additionally, odds ratios were calculated for the presence of both markers of insulin resistance compared with one marker or neither marker, using fit normal-weight individuals as the reference group, as shown in Supplementary Tables 1 and 2 (available online). Further, adjustment for African American ethnicity did not substantially change the results (Supplementary Tables 3 and 4, available online).
Figure.
Odds ratio for the presence of both markers of insulin resistance (impaired fasting glucose and triglycerides ≥150 mg/dL) versus neither marker for (A) women and (B) men, using normal weight fit individuals as the reference group and adjusting for age, smoking, and low-density lipoprotein cholesterol. *P = .001. **P < .001.
To directly assess the impact of fitness within each weight category, odds ratios were calculated using fit individuals in each weight category as the reference (Table 3). Obese women who were unfit had 1.9-fold higher odds of having both markers of insulin resistance compared with obese women who were fit (1.4–2.5; P <.001). Obese men who were unfit had a 1.7-fold higher odds of having both markers of insulin resistance compared with obese men who were fit (1.5–2; P <.001). With lower weight categories, the calculated odds ratios trended higher (Table 3).
Table 3.
Odds Ratios for Both Markers of Insulin Resistance (Impaired Fasting Glucose and Triglycerides ≥150 mg/dL) versus Neither Marker, Comparing Unfit and Fit Individuals Within Each Weight Category, Adjusting for Age, Smoking, and LDL-C
| Odds Ratio (95% Confidence Interval) | P Value | |
|---|---|---|
| Women | ||
| Normal weight | 2.2 (1.4–3.6) | .001 |
| Overweight | 2.1 (1.5–3.0) | <.001 |
| Obese | 1.9 (1.4–2.5) | <.001 |
| Men | ||
| Normal weight | 2.8 (2.1–3.6) | <.001 |
| Overweight | 2.4 (2.1–2.7) | <.001 |
| Obese | 1.7 (1.5–2.0) | <.001 |
LDL-C = low-density lipoprotein cholesterol.
DISCUSSION
We found a significant association between poor fitness and increased risk for carrying markers of insulin resistance. This association existed for both women and men in all weight categories. Interestingly, the absolute difference in odds ratios between unfit and fit individuals was magnified in higher weight categories, particularly for women. These results add to our understanding of how cardiorespiratory fitness impacts health, and they support the notion that some of the heterogeneity in insulin resistance previously observed across weight groups may be accounted for by fitness.
Furthermore, these results add to prior observations regarding cardiorespiratory fitness and health outcomes. Otherwise healthy individuals with poor cardiorespiratory fitness have been shown to be at increased risk for both cardiovascular mortality23,24 and all-cause mortality,19,24 and poor fitness may be a stronger predictor of mortality in women than in men.25 Several plausible mechanisms likely contribute toward this association. In particular, physical fitness is associated with lower rates of hypertension and diabetes, decreased inflammation, improved endothelial function, fewer thrombotic events, and lower cancer risk.26,27 Our data support the hypothesis that one mechanism by which poor fitness may cause worse cardiovascular outcomes is through the development of insulin resistance.
These results also highlight the potential benefit of attaining normal fitness for preventing insulin resistance in otherwise healthy individuals. Obese individuals, particularly women, may stand to benefit the most from achieving at least moderate fitness. Clearly both fitness and adiposity are factors in the development of insulin resistance. However, for many obese patients, reaching moderate fitness may be a more obtainable goal than significant sustained weight loss.
Finally, our results also highlight the importance of fitness in normal weight individuals. We observed a more than 2-fold increased odds for markers of insulin resistance in apparently healthy and normal weight individuals who were unfit compared to those who were fit. This finding supports the role of fitness counseling at well-patient visits, even for normal weight patients.
A unique strength of this study is the use of an objective and standardized measure of fitness. Moreover, we utilized markers of insulin resistance that are routinely measured in clinical practice, making these results easily translatable. Another important strength is the large sample size, including a large cohort of women.
A primary limitation of this study is its design as a cross-sectional analysis. We observed an association between the study variables at a single time point. No causal inferences or outcome predictions could be made. Additionally, we could not directly measure insulin resistance with the available data, and instead relied on previously validated clinical markers.16 However, current methods do not allow for direct measurements of insulin resistance in normal clinical practice, and other proposed surrogates are not well standardized.28
Another limitation of this study is the lack of racial/ethnic diversity in the participants. Prior work has suggested that the association between triglycerides and insulin resistance may be less pronounced in African Americans than in whites.29 Adjusting for African American ethnicity did not impact our results. However, the study cohort included relatively few participants who self-reported as African American.
In conclusion, poor fitness is associated with an increased risk of insulin resistance in both genders, independent of BMI. The relative impact of fitness and weight may be greater for women compared with men. Future work will be necessary to assess the longitudinal relationship between cardiorespiratory fitness and insulin resistance. Finally, additional work is needed to better understand fitness and insulin resistance in non-white populations.
Supplementary Material
CLINICAL SIGNIFICANCE.
Poor cardiorespiratory fitness is associated with elevated markers of insulin resistance in healthy non-diabetic adults, independent of weight.
The benefit of fitness in terms of reducing risk for insulin resistance may be greatest among obese individuals.
The association between fitness, weight, and markers of insulin resistance is present in both genders, but the patterns of risk suggest gender differences exist.
Acknowledgments
Funding: JWK is supported by the Stanford Diabetes Research Center (P30DK116074), the American Diabetes Association (1-19-JDF-108), and the National Institute of Diabetes and Digestive and Kidney Diseases (1R01DK107437, 1R01DK106236, 1R01DK11675001A1).
Footnotes
Conflict of Interest: None.
SUPPLEMENTARY DATA
Supplementary data to this article can be found online at https://doi.org/10.1016/j.amjmed.2019.11.031.
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