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Body mass index (BMI, kg/m^{2}) may not be the best marker for estimating the risk of obesity-related disease. Consistent with physiologic observations, an alternative index uses waist circumference (WC) and fasting triglycerides (TG) concentration to describe lipid overaccumulation.

The WC (estimated population minimum 65 cm for men and 58 cm for women) and TG concentration from the third National Health and Nutrition Examination Survey (N = 9,180, statistically weighted to represent 100.05 million US adults) were used to compute a "lipid accumulation product" [LAP = (WC-65) × TG for men and (WC-58) × TG for women] and to describe the population distribution of LAP. LAP and BMI were compared as categorical variables and as log-transformed continuous variables for their ability to identify adverse levels of 11 cardiovascular risk factors.

Nearly half of the represented population was discordant for their quartile assignments to LAP and BMI. When 23.54 million with ordinal LAP quartile > BMI quartile were compared with 25.36 million with ordinal BMI quartile > LAP quartile (regression models adjusted for race-ethnicity and sex) the former had more adverse risk levels than the latter (p < 0.002) for seven lipid variables, uric acid concentration, heart rate, systolic and diastolic blood pressure. Further adjustment for age did not materially alter these comparisons except for blood pressures (p > 0.1). As continuous variables, LAP provided a consistently more adverse beta coefficient (slope) than BMI for nine cardiovascular risk variables (p < 0.01), but not for blood pressures (p > 0.2).

LAP (describing lipid overaccumulation) performed better than BMI (describing weight overaccumulation) for identifying US adults at cardiovascular risk. Compared to BMI, LAP might better predict the incidence of cardiovascular disease, but this hypothesis needs prospective testing.

Obesity is commonly understood to imply excess fat, but it is ordinarily classified according to excess weight. This semantic inconsistency may help to explain why the body mass index (BMI, kg/m^{2}) – a popular marker for relative weight – performs only modestly as a predictor of medical risk [

In the current era of increasing obesity, we should attempt to define and measure lipid accumulation specifically in those contexts where accumulation may represent a physiologic danger [

This paper first describes a simple index for estimating lipid overaccumulation among adults. Next, to demonstrate the utility of the lipid overaccumulation concept, this paper tests the hypothesis that the described index is better correlated than BMI with a variety of cardiovascular risk factors. This hypothesis should not be surprising since the BMI can neither distinguish between fat and lean tissues nor identify the anatomic location or function of distinct fat depots.

The proposed index – designated the "lipid accumulation product" (LAP) – is based on a combination of two measurements that are safe and inexpensive to obtain. One is waist circumference (WC), a measure of truncal fat that includes the visceral (intra-abdominal) depot. The other is the fasting concentration of circulating triglycerides (TG), the esterified, long-chain fatty acids that circulate through blood contained stably inside lipoproteins. Both waist size and TG concentration tend to rise with age [

Although previous papers have proposed that the combination of enlarged waist and elevated TGs might serve as a dichotomous risk marker [

Data were obtained from the third National Health and Nutrition Examination Survey (NHANES III), a probability sample of the US civilian, noninstitutionalized population that included an oversample of non-Hispanic blacks and Mexican Americans [

Sampling weights from NHANES III were used with the software programs SAS, SAS/Graph (Release 8.2, SAS Institute, Cary, NC), and SUDAAN (Release 8.0, Research Triangle Institute, Research Triangle, NC), to estimate the sizes of the represented adult populations, to describe the distributions in the population of risk factors associated with LAP and BMI, and to perform analyses using multivariable linear regression. The analyses thus incorporated sampling weights that accounted for unequal selection probabilities (clustered design, planned oversampling, and differential nonresponse) [

Sex-specific bubble plots of population density by the values for WC (to the nearest cm) and TG concentration (to the nearest 0.1 mmol/L) were prepared to represent US adults in three age ranges (figure

The minimum WC values were used to define sex-specific origin points (near the left-lower corner of each panel in figure

LAP for men = (WC [cm] - 65) × (TG concentration [mmol/L])

LAP for women = (WC [cm] - 58) × (TG concentration [mmol/L])

In order to avoid having nonpositive values for LAP, any waist values for men that were 65 cm or less (five men in the NHANES III sample, all aged 18–22 years) were revised upward to 66.0 cm. No women in the entire NHANES III sample had a waist circumference less than 58.4 cm.

Starting with the estimated full population, two subpopulations were identified for which the quartile classifications for LAP and BMI were discordant (table ^{2}). All comparisons are reported with two-sided p values.

Distribution of US adults by population quartiles of lipid accumulation product and body mass index. Table shows number of survey participants and corresponding population estimates (millions, in parentheses). Participants identified in the bold-print cells are concordant for their quartile assignment both to LAP and BMI.

4 | 3 | 2 | 1 | |||

Quartiles of body mass index (BMI) | 4 | 809 (7.30) | 248 (2.26) | 21 (0.17) | 2621 (25.19) | |

3 | 686 (6.93) | 724 (7.28) | 164 (1.42) | 2557 (25.30) | ||

2 | 191 (2.23) | 531 (6.08) | 521 (6.93) | 1985 (24.77) | ||

1 | 34 (0.39) | 176 (1.95) | 494 (5.97) | 2017 (24.79) | ||

2454 (25.00) | 2499 (24.99) | 2208 (25.03) | 2019 (25.02) | 9180 (100.05) |

Linear regression models were prepared from the entire population (discordant and concordant) that used either log-transformed LAP (ln LAP) or log-transformed BMI (ln BMI) as continuous independent variables. The dependent (outcome) variables were the same set of 11 cardiovascular risk factors. Models were also prepared separately for two age groups, those under age 50 years and those aged 50+ years, with adjustments for race-ethnicity and for sex when the sexes were combined. For each outcome risk variable, ln LAP and ln BMI were evaluated by comparing the proportion of the total variation that each index could explain, that is, R^{2 }for the entire model minus R^{2 }for a base model that excluded ln LAP and ln BMI. For these continuous analyses, the beta coefficients (slopes) were standardized to reflect the increment in each outcome variable associated with an increment of one standard deviation from the mean (calculated for each sex and age group [18–49 years or 50+ years]) of either ln LAP or ln BMI.

The population-based distributions of LAP and BMI were skewed to the right (table

Population estimates of lipid accumulation product and body mass index by sex and age group. Estimates derived for US adults from NHANES III, 1988–1994.

Lipid accumulation product (LAP) | Body mass index (BMI) ^{2} | ||||||||

Survey sample (N) | Geometric mean | ^{th} | ^{th} | ^{th} | Geometric mean | ^{th} | ^{th} | ||

Men | |||||||||

18–24 years | 685 | 16.2 | 9.2 | 15.5 | 27.6 | 23.6 | 21.1 | 23.1 | 25.7 |

25–49 years | 1982 | 35.0 | 20.1 | 35.5 | 63.2 | 26.4 | 23.7 | 25.9 | 29.0 |

50+ years | 1780 | 52.4 | 33.3 | 53.4 | 85.6 | 26.9 | 24.3 | 26.8 | 29.9 |

Women | |||||||||

18–24 years | 715 | 16.6 | 9.4 | 16.0 | 27.6 | 23.1 | 20.0 | 22.4 | 25.6 |

25–49 years | 2216 | 25.7 | 13.6 | 24.6 | 47.7 | 25.4 | 21.5 | 24.6 | 29.5 |

50+ years | 1802 | 50.2 | 29.6 | 51.7 | 84.5 | 26.9 | 23.1 | 26.4 | 30.7 |

All | |||||||||

18–24 years | 1400 | 16.4 | 9.2 | 15.7 | 27.6 | 23.4 | 20.6 | 22.8 | 25.6 |

25–49 years | 4198 | 30.1 | 16.4 | 29.7 | 57.3 | 25.9 | 22.5 | 25.3 | 29.1 |

50+ years | 3582 | 51.2 | 31.1 | 52.6 | 85.3 | 26.9 | 23.8 | 26.6 | 30.3 |

Applied to the entire adult age range, the male and female quartile cutpoints for LAP were similar, although the men's values were slightly higher than the women's (figure

The linear correlation between LAP and BMI for the adult population was modest (r = 0.58) and somewhat stronger when both indices were log-transformed (r = 0.71).

Compared to the subpopulation with ordinal BMI quartile > ordinal LAP quartile, the subpopulation with ordinal LAP quartile > ordinal BMI quartile was older [50.5 (SE 0.7) years vs. 38.5 (0.6) years], had more non-Hispanic whites [82.2 (1.8) vs. 69.4 (2.0) percent], and fewer non-Hispanic blacks [5.2 (0.5) vs. 17.5 (1.3) percent] and Mexican Americans [4.4 (0.5) vs. 5.9 (0.6) percent]. In regression models adjusted for sex and race-ethnicity (table

Mean levels of cardiovascular risk variables among the subpopulations discordant for quartiles of LAP and BMI. Estimates derived for US adults from NHANES III, 1988–1994.

Mean (SE) adjusted for sex and race-ethnicity | Mean (SE) adjusted for sex, race-ethnicity, and age | ||||||

Dependent variable, | Discordant survey sample (N) | LAP quartile > BMI quartile | BMI quartile > LAP quartile | P value | LAP quartile > BMI quartile | BMI quartile > LAP quartile | P value |

Total cholesterol, | 4598 | 5.62 (0.04) | 4.89 (0.04) | <0.0001 | 5.50 (0.04) | 5.00 (0.04) | <0.0001 |

HDL cholesterol, | 4578 | 1.24 (0.01) | 1.37 (0.01) | <0.0001 | 1.22 (0.01) | 1.39 (0.01) | <0.0001 |

LDL cholesterol, | 3333 | 3.50 (0.04) | 3.11 (0.05) | <0.0001 | 3.40 (0.04) | 3.20 (0.05) | 0.0003 |

Total cholesterol / HDL cholest. | 4577 | 4.93 (0.06) | 3.78 (0.05) | <0.0001 | 4.88 (0.06) | 3.82 (0.05) | <0.0001 |

Apolipoprotein B, | 2245* | 1.13 (0.01) | 0.94 (0.01) | <0.0001 | 1.10 (0.01) | 0.97 (0.01) | <0.0001 |

ApoB/ApoA1 | 2230* | 0.809 (0.012) | 0.681 (0.010) | <0.0001 | 0.799 (0.013) | 0.690 (0.010) | <0.0001 |

LDL cholesterol /ApoB, | 1614* | 3.06 (0.04) | 3.26 (0.03) | 0.0007 | 3.03 (0.04) | 3.29 (0.03) | <0.0001 |

Uric acid, | 4533 | 327 (2) | 311 (3) | <0.0001 | 325 (2) | 313 (3) | 0.0004 |

Systolic blood pressure, | 4595 | 124.9 (0.7) | 118.3 (0.4) | <0.0001 | 121.3 (0.6) | 121.6 (0.4) | 0.62 |

Diastolic blood pressure, | 4595 | 74.5 (0.3) | 73.0 (0.3) | 0.0018 | 74.1 (0.3) | 73.4 (0.3) | 0.14 |

Heart rate, | 4494 | 75.0 (0.5) | 72.4 (0.6) | 0.0005 | 75.1 (0.5) | 72.3 (0.6) | 0.0001 |

* Lipoprotein data obtained only during phase 1 of NHANES III.

Ln LAP consistently explained a greater portion of the variation of the outcome variables than did ln BMI for all seven lipid outcome variables, uric acid concentration, and heart rate (figure ^{2}) by ln LAP was about twice that of ln BMI. For the remaining two variables, systolic and diastolic blood pressure, the contrasts in portion of explained variation were small but consistent between the sexes. In the population under age 50 years, ln BMI was the better predictor of systolic blood pressure, but ln LAP was better for diastolic pressure. In the population aged 50+ years, ln LAP was the stronger predictor of systolic blood pressure.

^{2}) in risk variables explained by ^{2 }values for sex-specific, age-specific, regression models of cardiovascular risk variables after adjustment for race-ethnicity.

A similar pattern was seen in a comparison of the standardized beta coefficients applied to the entire adult age range (data not shown). The beta coefficient (slope) of ln LAP was consistently more adverse (p < 0.01) than that of ln BMI for nine of the cardiovascular risk variables, but not for systolic and diastolic blood pressures (p > 0.2). When these comparative models were stratified by the two age groups the relatively greater slope of ln LAP was preserved among the nine variables for both age groups (table

Estimated increments in each risk variable per 1 standard deviation of ln LAP or ln BMI. Population estimates derived for US adults from NHANES III (1988–1994) with adjustments for sex and race-ethnicity.

Dependent | Age (years) | Mean outcome | Observations (N) used in | Increment (SE) per 1 SD in ln LAP | Increment (SE) per 1 SD in ln BMI |

Total cholesterol | 18–49 | 4.96 | 5597 | 0.413 (0.023) | 0.225 (0.018)*** |

50+ | 5.75 | 3582 | 0.320 (0.024) | 0.089 (0.022)*** | |

HDL cholesterol | 18–49 | 1.29 | 5571 | -0.152 (0.008) | -0.111 (0.007)** |

50+ | 1.32 | 3570 | -0.187 (0.009) | -0.109 (0.010)*** | |

LDL cholesterol | 18–49 | 3.09 | 3858 | 0.326 (0.023) | 0.220 (0.021)** |

50+ | 3.62 | 2754 | 0.146 (0.031) | 0.058 (0.026) | |

Total cholesterol/HDL cholest. | 18–49 | 4.16 | 5570 | 0.905 (0.040) | 0.584 (0.033)*** |

50+ | 4.74 | 3570 | 0.930 (0.042) | 0.383 (0.042)*** | |

Apolipo-protein B | 18–49 | 0.97 | 2668† | 0.123 (0.006) | 0.074 (0.007)*** |

50+ | 1.16 | 1807† | 0.115 (0.010) | 0.056 (0.007)*** | |

ApoB/ApoA1 | 18–49 | 0.717 | 2642† | 0.115 (0.006) | 0.074 (0.006)*** |

50+ | 0.810 | 1801† | 0.109 (0.009) | 0.069 (0.006)** | |

LDL cholesterol/ApoB | 18–49 | 3.15 | 1813† | -0.090 (0.017) | -0.013 (0.012)** |

50+ | 3.19 | 1393† | -0.160 (0.018) | -0.066 (0.022)** | |

Uric acid | 18–49 | 315 | 5524 | 27 (1) | 25 (1) |

50+ | 334 | 3506 | 30 (2) | 23 (2)* | |

Systolic blood pressure | 18–49 | 115.1 | 5594 | 3.8 (0.2) | 3.9 (0.2) |

50+ | 134.1 | 3580 | 3.1 (0.3) | 2.0 (0.4) | |

Diastolic blood pressure | 18–49 | 72.9 | 5592 | 3.7 (0.2) | 3.3 (0.2) |

50+ | 75.5 | 3580 | 2.0 (0.2) | 2.0 (0.2) | |

Heart rate | 18–49 | 73.4 | 5482 | 2.4 (0.3) | 1.8 (0.2) |

50+ | 74.1 | 3493 | 2.2 (0.3) | 1.2 (0.3) |

* p < 0.01, ** p < 0.001, *** p < 0.0001, indicating significantly different beta coefficients for ln LAP and ln BMI.

† Lipoprotein data obtained only during phase 1 of NHANES III.

When the separate component measures that contribute to LAP (i.e., WC and TG concentration) were log-transformed and entered individually into predictive models, their standardized beta coefficients generally showed lesser or equivalent (p > 0.05) slopes compared to the slopes for LAP. The only exception was for the estimation of the ratio LDL cholesterol/Apo B among adults 18–49 years old. In this group the slope for ln TG alone [-0.160 (0.019)] was more steeply negative (p = 0.02) than the slope for ln LAP [-0.090 (0.017)].

The index described in this paper – the lipid accumulation product (LAP) – was developed in an effort to reflect the combined anatomic and physiologic changes associated with lipid overaccumulation in adults. Compared with BMI, LAP exhibited better correlations with lipid risk variables, uric acid concentration, and heart rate, but its correlation with blood pressure was roughly equivalent.

It is reasonable to speculate that the two LAP components – that is, enlarged abdominal fat depots and increased TG concentration – are each an indication that available lipid fuels have exceeded the individual's capacity to buffer and safely store this major form of acquired energy. Prior to 50 years old, the LAP appears to rise more slowly with age for women compared to men (Table

Whether an individual's excess lipid fuel appears eventually as an enlarged abdomen or as elevated circulating TG could be dictated in part by genes or by features of the individual's environmental circumstances. A special case, by way of an extreme example, might be the rare individual who is genetically disposed to extremely high TG concentrations (chylomicronemia). For the purpose of risk assessment in the general adult population, however, the alternative manifestations of lipid overaccumulation could be similarly informative. Regardless if the overaccumulation is marked by waist size, by TG concentration, or by both, the calculated value of LAP will be increased. In parallel with the LAP increments, excess lipid material will increasingly be deposited in nonadipose, "ectopic" tissues (e.g., liver, skeletal muscle, heart, blood vessels, kidneys, and pancreas) where it may adversely modify cellular metabolism, accelerate apoptosis (cell death), and interfere with cardiovascular control [

Ectopic lipid deposition is difficult to quantify directly, but an increased LAP value may indicate that various tissues or organs have become more vulnerable to injury from lipid overaccumulation. Lipoprotein particles with small diameters are more associated with disease risk than those with large diameters [

In contrast to an elevation in LAP value, an elevated BMI value (i.e., relative weight) is less specific in its anatomic or physiologic implications. Increased weight might represent enhancement of lean tissues, enlargement of the protective, subcutaneous adipose depots in the lower extremities [

In order for LAP to gain a useful role in clinical medicine or epidemiology, at least three major questions remain to be addressed:

The cross-sectional associations with LAP demonstrated in this paper should be seen primarily as a demonstration of how the concept of lipid overaccumulation may be expressed in an adult population. The utility of LAP for research or as a practical tool for use in the community will depend on the degree to which LAP can be demonstrated to enhance prediction of disease incidence. Prospective data sets that include baseline information on WC and fasting TG concentration would be well suited to evaluate LAP as a predictor of cardiovascular outcomes and mortality.

ApoA1, apolipoprotein A1

ApoB, apolipoprotein B

BMI, body mass index

HDL, high-density lipoprotein

LAP, lipid accumulation product

LDL, low density lipoprotein

NHANES III, third National Health and Nutrition Examination Survey

The author(s) declares that he has no competing interests.

HK conceived of this study, performed the calculations, and drafted the manuscript.

The pre-publication history for this paper can be accessed here:

The author acknowledges the extraordinary efforts of the field staff, laboratory personnel, and statisticians who collected and processed the information in NHANES III.