Long-term changes in body composition and their relationships with cardiometabolic risk factors: A population-based cohort study

Zhaoyang Fan; Yunping Shi; Guimin Huang; Dongqing Hou; Junting Liu

doi:10.1371/journal.pone.0251486

. 2021 May 13;16(5):e0251486. doi: 10.1371/journal.pone.0251486

Long-term changes in body composition and their relationships with cardiometabolic risk factors: A population-based cohort study

Zhaoyang Fan ¹, Yunping Shi ², Guimin Huang ³, Dongqing Hou ³, Junting Liu ^3,^*

Editor: Y Zhan⁴

PMCID: PMC8118322 PMID: 33984012

Abstract

The aim of the present study was to classify the latent body fat trajectories of Chinese adults and their relationships with cardiometabolic risk factors. Data were obtained from the China Health Nutrition Survey for 3,013 participants, who underwent six follow-up visits between 1993 and 2009. Skinfold thickness and other anthropometric indicators were used to estimate body composition. The latent growth model was used to create fat mass to fat-free mass ratio (F2FFMR) trajectory groups. Blood pressure, fasting plasma glucose, total cholesterol, triglycerides, and high- and low-density lipoprotein–cholesterol were measured in venous blood after an overnight fast. Logistic regression was used to explore the relationships of F2FFMR trajectory with cardiometabolic risk factors. In men, four types of F2FFMR trajectory were identified. After adjustment for behavioral and lifestyle factors, age, and weight status, and compared with the Low stability group, the High stability group showed a significant association with diabetes. In women, three types of F2FFMR trajectory were identified. Compared to the Low stability group, the High stability group showed significant associations with diabetes and hypertension after adjustment for the same covariates as in men. Thus, in this long-term study we have identified three F2FFMR trajectory groups in women and four in men. In both sexes, the highly stable F2FFMR is associated with the highest risk of developing diabetes, independent of age and body mass. In addition, in women, it is associated with the highest risk of hypertension, independent of age and body mass.

1. Introduction

Obesity has become a major health problem [1], with the prevalence of overweight and obesity in adults reaching 36.9% and 38.0% in men and women, respectively [2]. Millions of deaths can be ascribed to obesity worldwide [3, 4]. Fat mass is a direct indicator for evaluating obesity, and obesity has trajectory effects [5], and longitudinal mixed-effects and latent growth curve models are commonly used to characterize changes in body mass index (BMI) and their relationships with subsequent outcomes [6–10]. Excessive fat mass has an adverse effect on cardiometabolic risk factors. However, there is a maximum capacity for adipose expansion, and when this is reached, lipids accumulate in other tissues and cause metabolic disease [11]. In contrast, fat-free mass protects against the development of cardiometabolic risk factors [12]. Therefore, we aimed to construct a metabolic load-capacity model, in which fat mass is the metabolic load and fat-free mass is the metabolic capacity [13, 14].

Percentage fat mass is usually used to assess cardiometabolic status, but this may be inappropriate mathematically; fat mass is the numerator, but is also included in the denominator [15]. Instead, the fat mass to fat-free mass ratio (F2FFMR) may be a superior indicator of the ability to maintain homoeostasis at the level of the organ or tissue. F2FFMR can be used for the prediction of metabolic risk in population-based studies [16]. However, it is not clear whether changes in F2FFMR have effects, and how fat mass and fat-free mass ratio changes. To date, some studies [17–19] explored the body composition changes based on small-scale survey and short-term follow-up in different groups of population, few studies have characterized the trajectories of F2FFMR and their associations with the development of cardiometabolic risk factors in adults based large-scale population-based longitudinal study in China.

We hypothesized that an accumulation of body fat over time would be associated with the development of cardiometabolic risk factors. In the present study, we aimed to evaluate the association of body composition trajectory with the prevalence of cardiometabolic risk factors (dyslipidemia, diabetes, and hypertension), to better inform obesity control and prevention.

2. Methods

2.1. Study design

We used data from the China Health and Nutrition Survey (CHNS) to characterize body composition trajectory. Data were accessed from the Carolina Population Center (http://www.cpc.unc.edu/projects/china). The CHNS is an ongoing, large-scale, open, longitudinal, household-based survey that is conducted in China [20]. Nine provinces were selected, and a multi-stage random cluster sampling method stratified by income was used in each province. The first wave of the CHNS was completed in 1993, which was followed by subsequent waves in 1997, 2000, 2004, 2006, and 2009. A detailed description of the survey has been published elsewhere [20]. This study was approved by the Institutional Review Board of the National Institute for Nutrition and Food Safety, China Center for Disease Control and Prevention, and the University of North Carolina at Chapel Hill. All the participants provided their written informed consent.

2.2. Study population

The study cohort comprised adults aged 18–60 years at baseline in 1993 for whom age, sex, and physical examination data [skinfold thickness, body mass, height, systolic blood pressure (SBP), and diastolic blood pressure (DBP)] were available. Participants who were pregnant at the time of the survey, for whom data were missing or biologically implausible, or who had cancer were excluded. Those for whom data from at least two rounds of the survey were available were included. Ultimately, 3,013 participants were studied, of whom 1,637 were women. A flow chart for the study is shown in Fig 1.

2.3. Measurement and definition of body fat mass

Skinfold thickness, height, and body mass were measured using standard protocols [21]. Skinfold thickness was measured three times using skinfold calipers over the triceps muscle on the right arm, and the mean value was used in analyses. Height was measured without shoes using a Seca stadiometer (Seca North America East, Hanover, MD, USA), and body mass was measured while wearing lightweight clothing using a calibrated beam balance. BMI was calculated by dividing body mass (in kilograms) by the square of height (in meters). Weight status was defined as BMI ≥24 kg/m² for overweight and ≥28 kg/m² for obesity, respectively. Body fat mass was estimated using equations that included BMI and skinfold thickness: for men, fat mass percentage (FMP) = (0.742 × BMI) + (0.950 × triceps skinfold) + (0.335 × age) − 20.0; and for women, FMP (%) = (0.730 × BMI) + (0.548 × triceps skinfold) + (0.270 × age) − 5.5 [22]. Fat mass was calculated as FMP × body mass, and fat-free mass was calculated as body mass − fat mass. F2FFMR was calculated by dividing fat mass by fat-free mass.

2.4. Cardiometabolic risk factors

SBP and DBP were measured three times using the right arm after 10 min of rest in a seated position, using mercury sphygmomanometers with appropriate cuff sizes [23], and the mean values were used in analyses. Hypertension was defined as SBP/DBP ≥140/90 mm Hg, or the use of antihypertensive drugs, or a self-reported diagnosis of hypertension [23, 24]. After an overnight fast, blood sample was collected and biochemical test was completed in 2009. Total cholesterol (TC), triglyceride (TG), and high- and low- density lipoprotein–cholesterol (HDL-C and LDL-C) were measured using the glycerol-phosphate oxidase method on a Hitachi 7600 automated analyzer (Tokyo, Japan). High TC was defined as ≥6.2 mmol/l, high TG as ≥2.3 mmol/l, high LDL-C as ≥4.1 mmol/l, and low HDL-C as <1.0 mmol/l. Dyslipidemia was defined as any of high TC, high TG, high LDL-C, or low HDL-C, according to the guidelines for the prevention and treatment of dyslipidemia in Chinese adults [25]. Fasting plasma glucose (FPG) was measured using the glucose oxidase-phenol and 4-aminophenazone method (Randox Laboratories Ltd., Crumlin, UK), and diabetes was defined as FPG ≥7.0 mmol/l or the use of anti-diabetic medication.

2.5. Covariates and definitions

Educational level was classified according to attendance at junior high school or below, high or technical school, or college and above. Physical activity was categorized as light, moderate, or heavy. Income was categorized according to individual net income per year as <$3,000, $3,000–$4,999 and ≥$5,000. Marital status was defined as married or single. Living conditions were defined as urban or rural. Smoking was defined as the use of any nicotine-based product in the preceding year, and the participants were classified as smokers, non-smokers, or former smokers. Alcohol consumption was defined using the previous year’s consumption, and participants were classified as drinkers, non-drinkers, and former drinkers. Age and weight status were also adjusted in the model simultaneously.

2.6. Statistical analysis

Body fat trajectory patterns were identified using the group‐based trajectory modeling method [26] and longitudinal body fat data. Model fitting and parameter estimation were performed using the maximum likelihood method. Specific trajectory patterns were identified using the Bayes information criterion (BIC) in the group‐based trajectory modeling [26, 27]. The most appropriate models were considered to be those that permitted the most homogeneous grouping of the individual patterns, selected from among those with low BIC values. The minimum sample size for each trajectory group was 3% of the total cohort. For both men and women, a quadratic model was selected, and four and three trajectory groups were identified, respectively (Table 1). According to the trends in each trajectory group between 1993 and 2009, they were labelled “Low stability”, “High stability”, “Increase and decrease”, and “Increasing” in men; and “Low stability”, “High stability”, and “Increasing” in women (Fig 2).

Table 1. Indices of goodness-of-fit for the latent class growth analysis.

Sex	Model	Model fit	2 groups	3 groups	4 groups	5 groups
Male	Linear	BIC	-7477.424	-7586.715	-7650.447	-7663.206
		Entropy	0.952	0.864	0.866	0.861
		LMR P-value	0.0095	0.0015	0.0032	0.4822
		BLRT P-value	0	1	1	1
		Smallest Prop.	3.50%	3.40%	1.20%	1.20%
	Quadratic	BIC	-7602.546	-7767.145	-7900.029	-7880.402
		Entropy	0.963	0.895	0.88	0.893
		LMR P-value	0.0029	0.0034	0.0069	0.0351
		BLRT P-value	0	0	1	1
		Smallest Prop.	3.70%	3.80%	3.60%	0.40%
Female	Linear	BIC	-14296.499	-14396.825	-14407.113	-14418.243
		Entropy	0.849	0.869	0.871	0.806
		LMR P-value	0	0	0.0043	0.0072
		BLRT P-value	0	0.99	1	1
		Smallest Prop.	8.50%	3.70%	1.60%	1.20%
	Quadratic	BIC	-14551.652	-14632.521	-14656.953	-14706.93
		Entropy	0.869	0.875	0.875	0.87
		LMR P-value	0.2383	0.2348	0.232	0.2323
		BLRT P-value	0	0.995	1	0.985
		Smallest Prop.	7.90%	3.50%	0.70%	0.70%

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In the “Low stability” groups, F2FFMR remained low and did not vary substantially. In the “High stability” groups, F2FFMR remained high and did not vary substantially. In the “Increase and decrease” group, F2FFMR started low, then increased, before decreasing to a low level again. In the “Increasing” groups, F2FFMR increased, from low to high. Each participant was assigned to one of these groups and their basic characteristics were compared using the chi‐square test for categorical variables and the ANOVA F test for continuous variables. Multinomial logistic regression was used to assess the relationships between the body composition trajectory group and cardiometabolic risk factors, with the Low stability group as the reference. All the analyses were stratified according to sex. In addition, all the socioeconomic, demographic, and lifestyle covariates were included in the final multivariate analysis model. Age and weight status at the final visit were included as covariates in the adjusted analysis. SAS 9.4 (Cary, NC, USA) was used for the data analysis and trajectory analysis was performed using Mplus 8.3 software (Los Angeles, CA, USA). All the statistical tests were two‐sided, and P≤0.05 was regarded as indicating statistical significance.

3. Results

A total of 3,013 individuals (1,637 women and 1,376 men) were included in the study. The men were allocated to the following groups: 83.9% Low stability, 3.6% High stability, 6.8% Increase and decrease, and 5.7% Increasing. There were no differences in marital status or alcohol consumption among these groups, but there were differences in age, location, educational level, individual net income, smoking, physical activity, body mass, and FMP. The women were allocated to the following groups: 88.9% Low stability, 7.6% High stability, and 3.5% Increasing. There were no differences in educational level, individual net income, or alcohol consumption among these groups, but there were differences in the other factors (Tables 2 and 3).

Table 2. Characteristics of the male participants, according to their latent F2FFMR trajectory group.

Characteristics	F2FFMR trajectory group (n = 1376)				F/χ2	P value
n (%)/mean (SD)	Low stability	High stability	Increase and decrease	Increasing
n (%)/mean (SD)	(n = 1155)	(n = 49)	(n = 94)	(n = 78)
Age (years), mean (SD)
1993	37.8(10.5)	45.1(9.2)	43.0(9.1)	39.5(10.6)	14.6	<0.001
2009	53.7(10.5)	61.1(9.2)	59.0(9.1)	55.5(10.6)	14.8	<0.001
Married	1071(92.7)	44(89.8)	90(95.7)	71(91)	2.23	0.526
Location
Rural	898(77.8)	29(59.2)	69(73.4)	62(79.5)	10.064	0.018
Urban	257(22.2)	20(40.8)	25(26.6)	16(20.5)
Education
Lower than junior high school	941(81.5)	34(69.4)	71(75.5)	58(74.4)	43.5	<0.001
High or technical school	145(12.6)	1(2.0)	11(11.7)	11(14.1)
College and above	69(6.0)	14(28.6)	12(12.8)	9(11.5)
Individual net income per year
<$3000	721(80.6)	22(57.9)	59(73.8)	51(79.7)	13.7	0.033
$3000–$4999	109(12.2)	10(26.3)	15(18.8)	8(12.5)
≥$5000	65(7.3)	6(15.8)	6(7.5)	5(7.8)
Smoking
No	59(5.8)	1(2.4)	8(9.9)	5(6.9)	14.4	0.026
Yes	745(72.9)	27(65.9)	45(55.6)	48(66.7)
Quit	218(21.3)	13(31.7)	28(34.6)	19(26.4)
Alcohol consumption
No	101(8.8)	3(6.1)	6(6.4)	8(10.3)	3.91	0.688
Yes	708(61.4)	30(61.2)	52(55.3)	48(61.5)
Quit	345(29.9)	16(32.7)	36(38.3)	22(28.2)
Physical activity
Light	408(35.7)	26(54.2)	44(48.9)	32(42.1)	17.897	0.006
Moderate	171(15.0)	9(18.8)	14(15.6)	7(9.2)
Heavy	563(49.3)	13(27.1)	32(35.6)	37(48.7)
Weight status 2009
BMI <24	736(66.4)	10(21.3)	29(32.2)	31(40.3)	134	<0.001
BMI≥24, <28	328(29.6)	28(59.6)	39(43.3)	32(41.6)
BMI ≥28	44(4.0)	9(19.2)	22(24.4)	14(18.2)
FMP, mean (SD)
1993	15.9(6.6)	36.5(4.8)	20.9(10.2)	17.6(7.5)	142.97	<0.001
2009	26.9(7.9)	38.1(10.0)	31.2(7.8)	45.8(4.5)	167.26	<0.001

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Table 3. Characteristics of the female participants, according to their latent F2FFMR trajectory group.

Characteristics	F2FFMr trajectory group (n = 1637)			F/χ2	P value
n (%)/mean (SD)	Low stability	High stability	Increasing
n (%)/mean (SD)	(n = 1456)	(n = 124)	(n = 57)
Age (years), mean (SD)
1993	38.9(9.6)	47.1(7.9)	41.9(8.1)	44.1	<0.001
2009	54.9(9.6)	63.0(7.9)	57.8(8.1)	43.8	<0.001
Married	1275(87.6)	98(79.0)	48(84.2)	7.617	0.022
Location
Rural	1120(76.9)	83(66.9)	47(82.5)	7.530	0.023
Urban	336(23.1)	41(33.1)	10(17.5)
Education
Lower than junior high school	1314(90.3)	112(90.3)	53(93.0)	9.379	0.052
High or technical school	104(7.1)	4(3.2)	2(3.5)
College and above	38(2.6)	8(6.5)	2(3.5)
Individual net income per year
< $3000	899(88.6)	67(87.0)	29(80.6)	2.669	0.615
$3000 –$4999	74(7.3)	7(9.1)	4(11.1)
≥$5000	42(4.1)	3(3.9)	3(8.3)
Smoking
No	744(89.9)	49(73.1)	17(60.7)	37.089	<0.001
Yes	37(4.5)	8(11.9)	6(21.4)
Quit	47(5.7)	10(14.9)	5(17.9)
Alcohol consumption
No	1029(71.1)	83(69.2)	42(73.7)	1.977	0.74
Yes	119(8.2)	14(11.7)	4(7.0)
Quit	299(20.7)	23(19.2)	11(19.3)
Physical activity
Light	664(46.3)	89(71.8)	34(59.7)	33.318	<0.001
Moderate	162(11.3)	8(6.5)	7(12.3)
Heavy	607(42.4)	27(21.8)	16(28.1)
Weight Status 2009
BMI <24	865(61.4)	28(23.5)	0	285.679	<0.001
BMI≥24, <28	440(31.2)	48(40.3)	18(33.3)
BMI≥28	105(7.5)	43(36.1)	36(66.7)
FMP, mean (SD)
1993	27.2(5.0)	39.5(3.1)	30.5(4.6)	363.8	<0.001
2009	35.2(5.3)	42.2(5.5)	47.9(4.8)	236.4	<0.001

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In logistic regression models 1–4, cardiometabolic risk factors were used as dependent variables and trajectory group was the independent variable. The Low stability group was the reference group in each model. In men, high TC and high LDL-C were not associated with F2FFMR trajectory group, regardless of whether they were adjusted for covariates or not. Prior to adjustment for any covariates, the Increase and decrease trajectory group was significantly associated with high TG [crude odds ratio (OR) 1.65, 95% confidence interval (CI) 1.00–2.70)]. When adjusted for age in 2009, the Increase and decrease trajectory group remained significantly associated with high TG (crude OR 1.87, 95% CI 1.13–3.10). However, when further adjusted for body mass in 2009, this association disappeared. The High stability group was significantly associated with low HDL-C (crude OR 2.06, 95% CI 1.00–4.24) prior to adjustment, and after adjustment for age in 2009, the adjusted OR and 95% CI were 2.37 (1.13–4.94). However, after further adjustment for body mass status in 2009, this association disappeared.

The Increase and decrease group was significantly associated with dyslipidemia prior to adjustment (crude OR, 95% CI: 1.64, 1.07–2.53), and after adjustment for age in 2009 (adjusted OR, 95% CI: 1.79, 1.16–2.76). In addition, the High stability group was significantly associated with dyslipidemia after adjustment for age in 2009 (adjusted OR, 95% CI: 1.95, 1.08–3.51). However, after further adjustment for body mass in 2009, this association disappeared. The High stability group and the Increase and decrease group were significantly associated with diabetes after adjustment for age and body mass in 2009 (adjusted OR, 95% CI: 2.68, 1.31–5.51 and 1.90, 1.04–3.46, respectively). After further adjustment for educational level, smoking, alcohol consumption, location, marital status, and physical activity in model 4, the High stability group remained significantly associated (adjusted OR, 95% CI: 2.72, 1.25–5.92), but the association disappeared in the Increase and decrease group. After adjustment for age in 2009, the High stability group and the Increase and decrease group were also significantly associated with hypertension (adjusted OR, 95% CI: 2.48, 1.32–4.68 and 1.75, 1.12–2.72, respectively), but after further adjustment for other covariates, this association disappeared (Table 4).

Table 4. ORs and 95% CIs for the associations between the F2FFMR trajectories of men and cardiometabolic risk factors in 2009.

Cardiometabolic risk factors	F2FFMR trajectory group	Model 1,OR(95%CI)	Model 2,OR(95%CI)	Model 3,OR(95%CI)	Model 4,OR(95%CI)
High TC
	Low stability	Reference	Reference	Reference	Reference
	High stability	0.53(0.13,2.21)	0.54(0.12,2.27)	0.45(0.11,1.95)	0.23(0.03,1.73)
	Increase and decrease	1.00(0.45,2.23)	1.01(0.45,2.27)	0.87(0.38,2.00)	0.82(0.35,1.93)
	Increasing	1.42(0.66,3.05)	1.43(0.66,3.06)	1.24(0.57,2.70)	1.06(0.46,2.42)
High TG
	Low stability	Reference	Reference	Reference	Reference
	High stability	1.65(0.84,3.21)	1.96(0.99,3.88)	1.11(0.54,2.29)	0.82(0.36,1.85)
	Increase and decrease	1.65(1.00,2.70)^*	1.87(1.13,3.10)^*	1.09(0.63,1.88)	1.06(0.59,1.88)
	Increasing	0.58(0.27,1.22)	0.60(0.28,1.28)	0.36(0.16,0.78)	0.32(0.14,0.74)
High LDL-C
	Low stability	Reference	Reference	Reference	Reference
	High stability	0.65(0.20,2.12)	0.66(0.20,2.17)	0.52(0.16,1.76)	0.50(0.14,1.71)
	Increase and decrease	0.80(0.36,1.78)	0.81(0.36,1.80)	0.65(0.29,1.50)	0.54(0.22,1.31)
	Increasing	1.58(0.81,3.09)	1.59(0.81,3.11)	1.37(0.69,2.72)	1.24(0.60,2.550
Low HDL-C
	Low stability	Reference	Reference	Reference	Reference
	High stability	2.06(1.00,4.24)^*	2.37(1.13,4.94)^*	1.37(0.63,2.96)	1.66(0.73,3.74)
	Increase and decrease	1.17(0.62,2.21)	1.29(0.68,2.44)	0.74(0.38,1.47)	0.69(0.33,1.43)
	Increasing	1.00(0.49,2.07)	1.04(0.51,2.14)	0.69(0.32,1.45)	0.57(0.24,1.35)
Dyslipidemia
	Low stability	Reference	Reference	Reference	Reference
	High stability	1.74(0.97,3.10)	1.95(1.08,3.51)^*	1.17(0.62,2.19)	1.01(0.51,2.01)
	Increase and decrease	1.64(1.07,2.53)^*	1.79(1.16,2.76)^*	1.12(0.69,1.79)	1.07(0.64,1.77)
	Increasing	1.09(0.67,1.79)	1.12(0.69,1.84)	0.73(0.43,1.24)	0.66(0.38,1.16)
Diabetes
	Low stability	Reference	Reference	Reference	Reference
	High stability	4.33(2.21,8.45)^*	3.70(1.87,7.32)^*	2.68(1.31,5.51)^*	2.72(1.25,5.92)^*
	Increase and decrease	3.03(1.75,5.25)^*	2.72(1.56,4.74)^*	1.90(1.04,3.46)^*	1.85(0.98,3.49)
	Increasing	0.48(0.15,1.55)	0.46(0.14,1.48)	0.35(0.11,1.16)	0.39(0.12,1.28)
hypertension
	Low stability	Reference	Reference	Reference	Reference
	High stability	3.40(1.84,6.30)^*	2.48(1.32,4.68)^*	1.53(0.79,2.96)	1.55(0.76,3.16)
	Increase and decrease	2.14(1.39,3.30)^*	1.75(1.12,2.72)^*	1.10(0.69,1.77)	1.06(0.64,1.76)
	Increasing	1.29(0.81,2.05)	1.20(0.74,1.94)	0.84(0.51,1.39)	0.92(0.55,1.55)

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*P<0.05. Model 1: unadjusted; Model 2: adjusted for age in 2009; Model 3: Model 2 + body mass in 2009; Model 4: Model 3 + smoking, drinking, physical activity, location, educational level, and marital status.

In women, prior to adjustment, the High stability group was associated with high TC (crude OR, 95% CI: 2.25, 1.44–3.51). The High stability and Increasing groups were significantly associated with high TG, with crude ORs (95% CIs) of 2.61 (1.75–3.89) and 2.64 (1.49–4.66), respectively. The High stability group was associated with high LDL-C (crude OR, 95% CI: 2.15, 1.39–3.30) and dyslipidemia (crude OR, 95% CI: 2.63, 1.81–3.81). In addition, the High stability and Increasing groups were significantly associated with diabetes (crude OR, 95% CI: 4.52, 2.89–7.08 and 2.44, 1.16–5.11, respectively) and hypertension (crude OR, 95% CI: 4.03, 2.72–5.98 and 2.89, 1.66–5.04, respectively) (Table 5).

Table 5. ORs and 95% CIs for the associations between the F2FFMR trajectories of women and cardiometabolic risk factors in 2009.

Cardiometabolic risk factors	F2FFMR trajectory group	Model 1,OR(95%CI)	Model 2,OR(95%CI)	Model 3,OR(95%CI)	Model 4,OR(95%CI)
High TC
	Low stability	Reference	Reference	Reference	Reference
	High stability	2.25(1.44,3.51)^*	1.62(1.02,2.58)^*	1.38(0.84,2.25)	1.68(0.88,3.23)
	Increasing	1.38(0.67,2.86)	1.25(0.60,2.60)	0.94(0.43,2.07)	0.96(.034,2.72)
High TG
	Low stability	Reference	Reference	Reference	Reference
	High stability	2.61(1.75,3.89)^*	2.41(1.60,3.65)^*	1.35(0.86,2.13)	1.23(0.66,2.27)
	Increasing	2.64(1.49,4.66)^*	2.57(1.45,4.54)^*	1.00(0.53,1.86)	1.02(.043,2.42)
High LDL-C
	Low stability	Reference	Reference	Reference	Reference
	High stability	2.15(1.39,3.30)^*	1.60(1.03,2.50)^*	1.37(0.86,2.20)	1.59(0.85,2.99)
	Increasing	1.33(0.66,2.68)	1.20(0.59,2.45)	0.93(0.44,1.97)	0.94(0.34,2.62)
Low HDL-C
	Low stability	Reference	Reference	Reference	Reference
	High stability	1.72(0.93,3.18)	1.92(1.01,3.63)^*	1.27(0.75,2.50)	1.29(0.52,3.21)
	Increasing	1.13(0.40,3.18)	1.17(0.41,3.32)	0.61(0.20,1.81)	0.22(0.03,1.77)
Dyslipidemia
	Low stability	Reference	Reference	Reference	Reference
	High stability	2.63(1.81,3.81)^*	2.22(1.52,3.25)^*	1.55(1.03,2.34)^*	1.47(0.84,2.56)
	Increasing	1.63(0.95,2.78)	1.54(0.89,2.64)	0.79(0.44,1.43)	0.91(040,2.09)
Diabetes
	Low stability	Reference	Reference	Reference	Reference
	High stability	4.52(2.89,7.08)^*	3.59(2.25,5.73)^*	3.17(1.91,5.27)^*	3.06(1.54,6.08)^*
	Increasing	2.44(1.16,5.11)^*	2.27(1.08,4.76)^*	1.79(0.80,4.05)	1.81(0.59,5.61)
hypertension
	Low stability	Reference	Reference	Reference	Reference
	High stability	4.03(2.72,5.98)^*	2.58(1.71,3.90)^*	1.63(1.05,2.53)^*	2.05(1.09,3.85)^*
	Increasing	2.89(1.66,5.04)^*	2.62(1.47,4.65)^*	1.19(0.64,2.21)	1.13(0.47,2.76)

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In women, after adjustment for age in 2009, the same associations were identified (Table 5). After further adjustment for body mass in 2009 in model 3, the associations with high TC, high TG, high LDL-C, and low HDL-C disappeared. There was a significant association between the High stability group and dyslipidemia (crude OR, 95% CI: 1.55, 1.03–2.34), but this association disappeared after adjustment for other covariates in model 4. The High stability group was also significantly associated with diabetes and hypertension (adjusted OR, 95% CI: 3.06, 1.54–6.08 and 2.05, 1.09–3.85, respectively) (Table 5).

4. Discussion

In the present study, we have identified four patterns of F2FFMR trajectory in men and three in women using data from a 16-year population-based cohort study. By comparing the risks of dyslipidemia, diabetes, and hypertension among the participants with these different patterns, we determined that the participants in the High stability group were at the highest risks of diabetes and hypertension. Through comparing impact of the high stability group, increasing group, increase and decrease group on diabetes. We speculate the effect of F2FFMR on cardiometabolic risk is likely to accumulate slowly, and high F2FFMR in early life might have an impact on diabetes in later life. Our findings show that the monitoring of F2MMR trajectory may help identify individuals who are at higher risk of diabetes and hypertension.

In the present study, we found dyslipidemia was not statistically associated with F2MMR trajectory after adjustment for age and weight status in men, lifestyle covariates in women. So dyslipidemia might be primarily determined by body mass in men, but by lifestyle in women. The body composition of men and women differs: adipose tissue is more likely to accumulate around the trunk and abdomen of men, but around the hips and thighs of women [28, 29]. Study of the F2FFMR indicates that the adverse effects of high fat mass can be offset by the protective effects of high fat-free mass. Specifically, high fat-free mass protects against high TC and high LDL-C [30]. However, F2FFMR reflects whole-body composition, and does not discriminate between the effects of body fat in differing locations. For example, excess accumulation of fat mass, especially in the upper body, is associated with dyslipidemia in normal-weight individuals [31]. In addition, visceral fat is a risk factor for dyslipidemia in men, and this effect is independent of the influence of BMI and waist circumference [32], but might not be a risk factor in women [33]. BMI is an independent risk factor for hypertension in both men and women, and high fat mass is associated with a higher risk of hypertension, even in non-obese populations [34]. In contrast, it has also been shown that a reduction in fat-free mass is more strongly associated with the normalization of blood pressure than a reduction in fat mass [35].

The present findings also suggest that the impact of F2FFMR trajectory on cardiometabolic outcomes is mediated by current body mass. A study of a cohort from birth has also shown that the trajectory of fat mass is associated with the development of cardiometabolic risk factors in adulthood and is affected by BMI in adulthood [36]. In both men and women, F2FFMR of Hight stability was associated with diabetes in the present study, and this association remained even after adjustment for the covariates and body mass. The fat-to-muscle ratio is associated with blood glucose [37] and visceral fat mass might predict the risk of prediabetes or diabetes [38]. Fat-free mass, which mainly consists of muscle, is protective against diabetes, and skeletal muscle plays an important role in the consumption and storage of glucose [39], the regulation of blood glucose, and the prevention of hyperglycemia [40]. Thus, people with low muscle mass are at a higher risk of developing type 2 diabetes than those with high muscle mass, and the lower the percentage muscle mass, the higher the risk of developing type 2 diabetes [41, 42]. Furthermore, visceral fat has an independent effect on cardiometabolic risk factors, such as abnormal lipid and glucose metabolism [30, 37]. However, it was not possible to analyze the effect of visceral fat mass alone in the present study.

In the present study we used data from the CHNS, which is a nationwide study that has been conducted for over 16 years. Therefore, this study is meaningful because it is the first study to use group‐based trajectory modeling method to analyze the change of F2FFMR in Chinese, and the results provide strong evidence for associations between long-term changes in body composition and the development of cardiometabolic risk factors. We first use the F2FFMR as a metabolic load-capacity indicator for study with the cardiometabolic outcome. The latent growth model was used to create the body fat trajectory groups. However, there were some limitations to the study. First, the fat mass and fat-free mass were estimated using a verified model that included BMI and upper arm skinfold thickness, rather than by direct measurement. Therefore, there may be some bias in the body composition data. Second, the trajectory groups, except for the Low stability group, were relatively small. Larger samples are required to verify the association of F2FFMR trajectory with dyslipidemia and other cardiometabolic risk factors. Third, blood pressure was measured once, whereas a clinical diagnosis of hypertension should be made on the basis of three measurements made on three different days. Due to unavailability of data, we could not distinguish between type 1 diabetes and type 2 diabetes in this study. Fourth, F2FFMR trajectory might be affected by differences in ethnicity and eating habits. The present study was of the Chinese adult population; therefore, the findings require confirmation in other populations.

In conclusion, four types of F2FFMR trajectory were identified in men and three in women. High stability trajectory of F2FFMR was associated with the highest risk of developing diabetes in men, and diabetes and hypertension in women, independent of age and current body mass. Our results also suggest that the association between F2FFMR trajectory and dyslipidemia and hypertension in men is mediated by current body mass.

Acknowledgments

This research used data from CHNS. We thank the National Institute for Nutrition and Health, China Center for Disease Control and Prevention, Carolina Population Center (P2C HD050924, T32 HD007168), the University of North Carolina at Chapel Hill, the NIH (R01-HD30880, DK056350, R24 HD050924, and R01-HD38700) and the NIH Fogarty International Center (D43 TW009077, D43 TW007709) for support for the CHNS data collection and analysis of files from 1989 to 2015 and future surveys, the China–Japan Friendship Hospital, the Ministry of Health for support for CHNS 2009, the Chinese National Human Genome Center in Shanghai since 2009, and the Beijing Municipal Center for Disease Prevention and Control since 2011. We also thank Mark Cleasby, PhD, from Edanz Group (https://en-author-services.edanz.com/) for editing a draft of this manuscript.

Data Availability

The datasets analyzed during the current study are available in the following website: http://www.cpc.unc.edu/projects/china/.

Funding Statement

Jt Liu received the fund of Beijing Hospitals Authority Youth Program, code: QML20191302(http://www.bjygzx.org.cn/). Zy Fan received the fund of National Key Research and Development Program of China, grant No. 2018YFC1002503(https://service.most.gov.cn/index/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9·1 million participants. Lancet. 2011; 377: 557–567. 10.1016/S0140-6736(10)62037-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014; 384: 766–781. 10.1016/S0140-6736(14)60460-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Wormser D, Kaptoge S, Di Angelantonio E, Wood AM, Pennells L, Thompson A et al. Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies. Lancet. 2011; 377: 1085–1095. 10.1016/S0140-6736(11)60105-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Berrington de Gonzalez A, Hartge P, Cerhan JR, Flint AJ, Hannan L, MacInnis RJ et al. Body-mass index and mortality among 1.46 million white adults. N Engl J Med. 2010; 363: 2211–2219. 10.1056/NEJMoa1000367 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Ward ZJ, Long MW, Resch SC, Giles CM, Cradock AL, Gortmaker SL. Simulation of Growth Trajectories of Childhood Obesity into Adulthood. N Engl J Med. 2017; 377: 2145–2153. 10.1056/NEJMoa1703860 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Fan H, Zhang X. Body mass index trajectory across childhood and subsequent risk of elevated blood pressure. J Clin Hypertens (Greenwich). 2020; 22: 1902–1907. 10.1111/jch.14001 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Huang F, Zhang M, Li Y, Li Z, Fang J, Guo K. The longitudinal trajectory of body mass index in the Chinese population: A latent growth curve analysis. PLoS One. 2018; 13: e0207845. 10.1371/journal.pone.0207845 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Liu J, Frongillo EA, Boghossian NS, Cai B, Zhou H, Hazlett LJ. Body mass index trajectories during the first year of life and their determining factors. American journal of human biology: the official journal of the Human Biology Council. 2019; 31. 10.1002/ajhb.23188 [DOI] [PubMed] [Google Scholar]
9.Liu JX, Liu JH, Frongillo EA, Boghossian NS, Cai B, Hazlett LJ. Body mass index trajectories during infancy and pediatric obesity at 6 years. Ann Epidemiol. 2017; 27: 708–715.e1. 10.1016/j.annepidem.2017.10.008 [DOI] [PubMed] [Google Scholar]
10.Mattsson M, Maher GM, Boland F, Fitzgerald AP, Murray DM, Biesma R. Group-based trajectory modelling for BMI trajectories in childhood: A systematic review. Obes Rev. 2019; 20: 998–1015. 10.1111/obr.12842 [DOI] [PubMed] [Google Scholar]
11.Brøns C, Grunnet LG. MECHANISMS IN ENDOCRINOLOGY: Skeletal muscle lipotoxicity in insulin resistance and type 2 diabetes: a causal mechanism or an innocent bystander? Eur J Endocrinol. 2017; 176: R67–R78. 10.1530/EJE-16-0488 [DOI] [PubMed] [Google Scholar]
12.Markus MRP, Stritzke J, Siewert U, Lieb W, Luchner A, Döring A et al. Variation in body composition determines long-term blood pressure changes in pre-hypertension: the MONICA/KORA (Monitoring Trends and Determinants on Cardiovascular Diseases/Cooperative Research in the Region of Augsburg) cohort study. J Am Coll Cardiol. 2010; 56: 65–76. 10.1016/j.jacc.2010.01.056 [DOI] [PubMed] [Google Scholar]
13.Siervo M, Prado CM, Mire E, Broyles S, Wells JCK, Heymsfield S et al. Body composition indices of a load-capacity model: gender- and BMI-specific reference curves. Public Health Nutr. 2015; 18: 1245–1254. 10.1017/S1368980014001918 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Wells JCK. The capacity-load model of non-communicable disease risk: understanding the effects of child malnutrition, ethnicity and the social determinants of health. Eur J Clin Nutr. 2018; 72: 688–697. 10.1038/s41430-018-0142-x [DOI] [PubMed] [Google Scholar]
15.Wells JCK, Victora CG. Indices of whole-body and central adiposity for evaluating the metabolic load of obesity. Int J Obes (Lond). 2005; 29: 483–489. 10.1038/sj.ijo.0802899 [DOI] [PubMed] [Google Scholar]
16.Powell M, Lara J, Mocciaro G, Prado CM, Battezzati A, Leone A et al. Association between ratio indexes of body composition phenotypes and metabolic risk in Italian adults. Clin Obes. 2016; 6: 365–375. 10.1111/cob.12165 [DOI] [PubMed] [Google Scholar]
17.Jiang Y, Zhang Y, Jin M, Gu Z, Pei Y, Meng P. Aged-Related Changes in Body Composition and Association between Body Composition with Bone Mass Density by Body Mass Index in Chinese Han Men over 50-year-old. PLoS One. 2015; 10: e0130400. 10.1371/journal.pone.0130400 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Ho SC, Wu S, Chan SG, Sham A. Menopausal transition and changes of body composition: a prospective study in Chinese perimenopausal women. Int J Obes (Lond). 2010; 34: 1265–1274. 10.1038/ijo.2010.33 [DOI] [PubMed] [Google Scholar]
19.Zhong R-H, Long J-A, Wang F, Chen S, Luo Y, Lu X-T et al. Association between serum choline and betaine concentrations and longitudinal changes of body composition in community-dwelling middle-aged and older Chinese adults. Appl Physiol Nutr Metab. 2020; 45: 737–744. 10.1139/apnm-2019-0778 [DOI] [PubMed] [Google Scholar]
20.Popkin BM, Du S, Zhai F, Zhang B. Cohort Profile: The China Health and Nutrition Survey—monitoring and understanding socio-economic and health change in China, 1989–2011. Int J Epidemiol. 2010; 39: 1435–1440. 10.1093/ije/dyp322 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Yan S, Li J, Li S, Zhang B, Du S, Gordon-Larsen P et al. The expanding burden of cardiometabolic risk in China: the China Health and Nutrition Survey. Obes Rev. 2012; 13: 810–821. 10.1111/j.1467-789X.2012.01016.x [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Lean ME, Han TS, Deurenberg P. Predicting body composition by densitometry from simple anthropometric measurements. Am J Clin Nutr. 1996; 63: 4–14. 10.1093/ajcn/63.1.4 [DOI] [PubMed] [Google Scholar]
23.Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003; 289: 2560–2572. 10.1001/jama.289.19.2560 [DOI] [PubMed] [Google Scholar]
24.Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL et al. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension. 2003; 42: 1206–1252. 10.1161/01.HYP.0000107251.49515.c2 [DOI] [PubMed] [Google Scholar]
25.Joint committee for guideline revision. 2016 Chinese guidelines for the management of dyslipidemia in adults. J Geriatr Cardiol. 2018; 15: 1–29. 10.11909/j.issn.1671-5411.2018.01.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Nagin D. Group-based modeling of development (Harvard University Press, Cambridge, Mass., London, 2005). [Google Scholar]
27.Lennon H, Kelly S, Sperrin M, Buchan I, Cross AJ, Leitzmann M et al. Framework to construct and interpret latent class trajectory modelling. BMJ Open. 2018; 8: e020683. 10.1136/bmjopen-2017-020683 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Lemieux S, Prud’homme D, Bouchard C, Tremblay A, Després JP. Sex differences in the relation of visceral adipose tissue accumulation to total body fatness. Am J Clin Nutr. 1993; 58: 463–467. 10.1093/ajcn/58.4.463 [DOI] [PubMed] [Google Scholar]
29.Power ML, Schulkin J. Sex differences in fat storage, fat metabolism, and the health risks from obesity: possible evolutionary origins. Br J Nutr. 2008; 99: 931–940. 10.1017/S0007114507853347 [DOI] [PubMed] [Google Scholar]
30.Xiao P, Cheng H, Yan Y, Liu J, Zhao X, Li H et al. High BMI with Adequate Lean Mass Is Not Associated with Cardiometabolic Risk Factors in Children and Adolescents. J Nutr. 2020; 10.1093/jn/nxaa328 [DOI] [PubMed] [Google Scholar]
31.Ito H, Nakasuga K, Ohshima A, Sakai Y, Maruyama T, Kaji Y et al. Excess accumulation of body fat is related to dyslipidemia in normal-weight subjects. Int J Obes Relat Metab Disord. 2004; 28: 242–247. 10.1038/sj.ijo.0802528 [DOI] [PubMed] [Google Scholar]
32.Rothney MP, Catapano AL, Xia J, Wacker WK, Tidone C, Grigore L et al. Abdominal visceral fat measurement using dual-energy X-ray: association with cardiometabolic risk factors. Obesity (Silver Spring). 2013; 21: 1798–1802. 10.1002/oby.20223 [DOI] [PubMed] [Google Scholar]
33.Tian Z, Li Y, Mao Z, Yu S, Wang Y, Liu X et al. Sex-specific relationship between visceral fat index and dyslipidemia in Chinese rural adults: The Henan Rural Cohort Study. Prev Med. 2018; 116: 104–111. 10.1016/j.ypmed.2018.09.009 [DOI] [PubMed] [Google Scholar]
34.Park SK, Ryoo J-H, Oh C-M, Choi J-M, Chung P-W, Jung JY. Body fat percentage, obesity, and their relation to the incidental risk of hypertension. J Clin Hypertens (Greenwich). 2019; 21: 1496–1504. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Ittermann T, Werner N, Lieb W, Merz B, Nöthlings U, Kluttig A et al. Changes in fat mass and fat-free-mass are associated with incident hypertension in four population-based studies from Germany. Int J Cardiol. 2019; 274: 372–377. 10.1016/j.ijcard.2018.09.035 [DOI] [PubMed] [Google Scholar]
36.Araújo J, Severo M, Barros H, Mishra GD, Guimarães JT, Ramos E. Developmental trajectories of adiposity from birth until early adulthood and association with cardiometabolic risk factors. Int J Obes (Lond). 2015; 39: 1443–1449. 10.1038/ijo.2015.128 [DOI] [PubMed] [Google Scholar]
37.Gamboa-Gómez CI, Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The fat-to-lean mass ratio, a novel anthropometric index, is associated to glucose metabolic disorders. Eur J Intern Med. 2019; 63: 74–78. 10.1016/j.ejim.2019.03.017 [DOI] [PubMed] [Google Scholar]
38.Jung SH, Ha KH, Kim DJ. Visceral Fat Mass Has Stronger Associations with Diabetes and Prediabetes than Other Anthropometric Obesity Indicators among Korean Adults. Yonsei Med J. 2016; 57: 674–680. 10.3349/ymj.2016.57.3.674 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Mizgier ML, Casas M, Contreras-Ferrat A, Llanos P, Galgani JE. Potential role of skeletal muscle glucose metabolism on the regulation of insulin secretion. Obes Rev. 2014; 15: 587–597. 10.1111/obr.12166 [DOI] [PubMed] [Google Scholar]
40.Moore MC, Cherrington AD, Wasserman DH. Regulation of hepatic and peripheral glucose disposal. Best Pract Res Clin Endocrinol Metab. 2003; 17: 343–364. 10.1016/s1521-690x(03)00036-8 [DOI] [PubMed] [Google Scholar]
41.Hong S, Chang Y, Jung H-S, Yun KE, Shin H, Ryu S. Relative muscle mass and the risk of incident type 2 diabetes: A cohort study. PLoS One. 2017; 12: e0188650. 10.1371/journal.pone.0188650 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Son JW, Lee SS, Kim SR, Yoo SJ, Cha BY, Son HY et al. Low muscle mass and risk of type 2 diabetes in middle-aged and older adults: findings from the KoGES. Diabetologia. 2017; 60: 865–872. 10.1007/s00125-016-4196-9 [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0251486.r001

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Y Zhan

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PONE-D-20-38516

Body Composition Trajectory and Association with Cardiometabolic Risk Factors: a Population-Based Cohort Study

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The manuscript will benefit from editing to tidy up grammar/spelling.

Reviewer #2: Overview

The authors present an interesting analysis which considers different patterns of change in fat to fat-free mass ratio (trajectories) across 16 years in a population-based cohort in China. They relate the different trajectories to cardiometabolic outcomes. I think the concept and the results are interesting however I have some major concerns about the discussion and some of the conclusions which have been drawn from the results.

Furthermore, it is sometimes difficult to follow the points being made (particularly in the introduction & discussion). It is essential that the whole manuscript undergoes a thorough proof reading to ensure that all statements are written correctly in English and make sense as they are written.

Introduction

Where the authors refer to ‘cardiovascular risk factors’, this includes dyslipidemia, diabetes, cholesterol and triglyceride measures; I would consider these as ‘cardiometabolic’ outcomes and suggest changing the general terminology throughout the manuscript.

It would be good to define the ‘trajectory effect’ more clearly and mention potential biological underpinning for the trajectory effect itself, not just the negative consequences of obesity.

F2MR, consider changing this to F2FFMR

Methods

The first paragraph states that the first wave was 1989, then the ‘study population’ paragraph says baseline was 1993- this is a bit confusing, when was baseline?

3013 were included- how many were originally seen? Or, put another way, how many were excluded? In the flow chart it looks like 2792 participants were seen in 1993, I’m not sure I understand how 3013 participants can be included in this analysis?

Please insert the reference for ‘guidelines for the prevention and treatment of dyslipidemia in Chinese adults’.

Was there any distinction between type-2 and type-1 diabetes?

Note to editor: The group‐based trajectory modelling method was applied to the longitudinal body fat data using maximum likelihood method for model fitting and parameter estimation. I believe these are sound methods, however, I am not familiar with using them in my own work and therefore cannot fully confirm if this is appropriate.

The following sentence is unclear: ‘According to the trends of each trajectory group, low stability, high stability, increase and decrease, and increasing were designated in men. In women, low stability, high stability and increasing groups were designated (Figure 2).’

In the text it could be clearer what each group means, i.e. ‘high stability’ means a high Fat to fat-free mass ratio without much variation between 1993 and 2009. Please clearly describe why fat to fat-free mass ratio was used and not just the fat % value?

Results

I’m concerned about the high number of individuals who fall into the ‘high stability’ group. I am not a statistician so I cannot say if this invalidates the statistical methods in any way. It would be worth having the analysis verified by a statistician.

What is the justification for the adjustment strategy in tables 4 and 5? Why not have an age adjusted model before adjustment for other confounders (education, marital status etc..)? For a better understanding of mediation by age or original weight status, these factors should be sequentially adjusted for in separate models.

In the conclusion the authors state ‘Our results also suggest that the impact of the adiposity trajectory on cardiometabolic profile is mediated by concurrent weight status.’ Is this a reference to tables 4 & 5 model 3? Could this association not also be mediated by age as age is included in model 3 along with weight status?

Figures 1 & 2: I can’t see the figure legends anywhere in the submitted manuscript? These should be provided.

Tables 2 & 3: the formatting is not quite right and makes the heading difficult to read.

Discussion

The discussion is not well written and as such it is quite hard to follow the discussion points. The findings are discussed fairly superficially, more insight into potential physiological underpinnings for the different F2MR trajectories is necessary. For example, why might the high-stability trajectory group be associated with hypertension in men, independent of concurrent age and weight status? There is repetition of statements throughout the discussion which is unnecessary.

Consider opening the discussion with a summary paragraph of the key results and why they are important/novel.

Paragraph 2: ‘dyslipidaemia was mainly affected by concurrent weight status’ I don’t think you can draw this conclusion without separating age/weight adjustment in model 3.

The limitation section should also include the high number of participants who fall into the low stability group and low numbers in the other groups and discussion of potential ethnic differences in body composition trajectories.

**********

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Reviewer #2: No

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PLoS One. 2021 May 13;16(5):e0251486. doi: 10.1371/journal.pone.0251486.r002

Author response to Decision Letter 0

7 Mar 2021

Response to the Reviewers’ Comments

PONE-D-20-38516

Long-term Changes in Body Composition and their Relationships with Cardiometabolic Risk Factors: A Population-Based Cohort Study

（Please note that the title has been changed from Body Composition Trajectory and Association with Cardiometabolic Risk Factors: a Population-Based Cohort Study to the above one）

We are grateful for the thoughtful reviews provided by the external referees and valuable comments from the editors. We have carefully addressed each recommendation, and the manuscript is considerably stronger as a result of the modifications outlined below.

The modified text is highlighted in tracked copy in the revised manuscript.

Requests from the reviewers:

Reviewer #1: Many thanks for the opportunity to review this manuscript. The comments below are intended as a constructive direction on improving the manuscript.

1. Q: The manuscript will benefit from editing to tidy up grammar/spelling.

A: Revised as suggested

We get help from Mark Cleasby, PhD, a native English speaker, from Edanz Group (https://en-author-services.edanzgroup.com/) for editing a draft of this manuscript .

Reviewer #2: Overview

1. Q: Furthermore, it is sometimes difficult to follow the points being made (particularly in the introduction & discussion). It is essential that the whole manuscript undergoes a thorough proof reading to ensure that all statements are written correctly in English and make sense as they are written.

A: Revised as suggested

We get help from Mark Cleasby, PhD, a native English speaker, from Edanz Group (https://en-author-services.edanzgroup.com/) for editing a draft of this manuscript .

2. Q: Introduction

A: Revised as suggested

3. Q: It would be good to define the ‘trajectory effect’ more clearly and mention potential biological underpinning for the trajectory effect itself, not just the negative consequences of obesity.

A: Revised as suggested (Please see page 3 and 4)

4. Q: F2MR, consider changing this to F2FFMR

A: Revised as suggested

5. Q: Methods

The first paragraph states that the first wave was 1989, then the ‘study population’ paragraph says baseline was 1993- this is a bit confusing, when was baseline?

A: Revised as suggested (Please see page 4)

6. Q: 3013 were included- how many were originally seen? Or, put another way, how many were excluded? In the flow chart it looks like 2792 participants were seen in 1993, I’m not sure I understand how 3013 participants can be included in this analysis?

A: In this study, we included the participants who had at least two rounds of data. So the data was a maximum set of all 6 rounds.

7. Q: Please insert the reference for ‘guidelines for the prevention and treatment of dyslipidemia in Chinese adults.

A: Revised as suggested (Please see page 6)

8. Q: Was there any distinction between type-2 and type-1 diabetes?

A: In this study, we could not identify type-2 or type-1 diabetes accurately, but this study was a population-based study and type-1 diabetes usually was found in children and adolescents. Our study population was mainly in adults.

9. Q: Note to editor: The group‐based trajectory modelling method was applied to the longitudinal body fat data using maximum likelihood method for model fitting and parameter estimation. I believe these are sound methods, however, I am not familiar with using them in my own work and therefore cannot fully confirm if this is appropriate.

A: The group‐based trajectory modelling method was usually used for trajectory of longitudinal quantitative data, some references were quoted in introduction of our article.

10. Q: The following sentence is unclear: ‘According to the trends of each trajectory group, low stability, high stability, increase and decrease, and increasing were designated in men. In women, low stability, high stability and increasing groups were designated (Figure 2).’In the text it could be clearer what each group means, i.e. ‘high stability’ means a high Fat to fat-free mass ratio without much variation between 1993 and 2009.

A: Revised as suggested (Please see page 7)

11. Q: Please clearly describe why fat to fat-free mass ratio was used and not just the fat % value?

A: Revised as suggested (Please see page 4).

12. Q: Results

A: We have asked a statistician for verification.

In table 4 and 5, the ORs of high stability are not very big and their CIs are narrow

And an article published in ‘Journal of clinical hypertension’ used the same method and its four groups number are 1045, 327,75 and 37, which is less than this study. Its sensitivity analysis was through Logistic regression.

Fan et, al. J Clin Hypertens. 2020;00:1–6. DOI: 10.1111/jch.14001

13. Q: What is the justification for the adjustment strategy in tables 4 and 5? Why not have an age adjusted model before adjustment for other confounders (education, marital status etc..)?

A: Revised as suggested (Please see page 4).

14. Q: For a better understanding of mediation by age or original weight status, these factors should be sequentially adjusted for in separate models.

A: Revised as suggested (Please see page 4).

15. Q: In the conclusion the authors state ‘Our results also suggest that the impact of the adiposity trajectory on cardiometabolic profile is mediated by concurrent weight status.’ Is this a reference to tables 4 & 5 model 3? Could this association not also be mediated by age as age is included in model 3 along with weight status?

A: Revised as suggested, we have separated age and weight status in model 2 and model 3.

16. Q: Figures 1 & 2: I can’t see the figure legends anywhere in the submitted manuscript? These should be provided.

A: Revised as suggested

17. Q: Tables 2 & 3: the formatting is not quite right and makes the heading difficult to read.

A: Revised as suggested

18. Q: Discussion

A: Revised as suggested

19. Q: Consider opening the discussion with a summary paragraph of the key results and why they are important/novel.

A: Revised as suggested

20. Q: Paragraph 2: ‘dyslipidaemia was mainly affected by concurrent weight status’ I don’t think you can draw this conclusion without separating age/weight adjustment in model 3.

A: Revised as suggested (please see page 11)

21. Q: The limitation section should also include the high number of participants who fall into the low stability group and low numbers in the other groups and discussion of potential ethnic differences in body composition trajectories.

A: Revised as suggested (Please see Page)

Attachment

Submitted filename: Response to the Reviewers.docx

Click here for additional data file.^{(20.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0251486.r003

Decision Letter 1

Y Zhan

28 Mar 2021

PONE-D-20-38516R1

Long-term Changes in Body Composition and their Relationships with Cardiometabolic Risk Factors: A Population-Based Cohort Study

PLOS ONE

Dear Dr. Liu,

Please submit your revised manuscript by May 12 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Y Zhan

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: No

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: I Don't Know

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: The English language has largely been improved however I still do not think this paper is very well written. There are some outstanding and additional issues:

Introduction:

Line 45/46: “Millions of deaths can be ascribed to obesity worldwide[3], because it is associated with the development of cardiometabolic risk factors, such as dyslipidemia, diabetes, and hypertension[4, 5].”

This is not quite accurate. Dyslipidaemia, T2DM and hypertension are states of cardiometabolic disruption which increase the risk of adverse cardiovascular events.

Line 46/47: “Fat mass is used to evaluate obesity[6]”

In ref #6 they defined obesity using a BMI cut-off, so I don’t think this statement makes sense.

Line 61/62: “To date, few studies have characterized the trajectories of body composition and their associations with the development of cardiometabolic risk factors.”

I would say quite a large number of studies have tracked changes in body composition in longitudinal studies, however, I am unsure if this has previously been done in a Chinese population? Or if it has been done using the estimation of F2FFMR highlighting the importance of this work. Any more specific previous work on this should be referenced.

Methods:

(Q5&6) It is still not clear to me, from the written description and from Figure 1, how the authors arrived at the final sample size. What are the numbers in the box at the top right corner of fig 1?

“Those for whom data from at least two rounds of the survey were available were included.” How can you have a trajectory if you only have 2 measurements and such a wide age-range in this study sample? this should be explained, a supplementary information file might be helpful if the description is too lengthy for the main manuscript.

When were the cardiometabolic outcomes measured (at which visit)?

Was waist-hip ratio measured in this study?

(Q8) This is not true. Type 1 Diabetes develops in childhood but is still present into adulthood.

Results:

Table 5, statistically significant ORs not highlighted bold, as was done in Table 4.

Discussion:

Lines 247-250: “The effect of F2FFMR on cardiometabolic risk is likely to accumulate slowly, and high F2FFMR in early life might have an impact on cardiometabolic health in later life.”

It is not clear if these statements are based on evidence presented in previously published work? Or are speculation.

Line 256/256: “Study of the F2FFMR indicates that the adverse effects of high fat mass can be offset by the protective effects of high fat-free mass.”

Line 267/268: “The present findings also suggest that the impact of F2FFMR trajectory on cardiometabolic outcomes is mediated by changes in body mass.”

Is it ‘changes in body mass’ or just ‘current weight’? I think you should revise your interpretation of your results.

Line 270: do you mean the ‘high stability’ group or do you mean ‘highly stable’?

Line 294: I think the study sample from a Chinese only population living in China could be considered a strength of your study, given the previously published data from other parts of the world. There are other strengths that merit discussion as well.

Line299/300: “Our results also suggest that the association between F2FFMR trajectory and the cardiometabolic profile is mediated by body mass.”

If the associations are all mediated by current body mass status, then why do we need to know about the trajectories? It looks like your data do not fully support this statement: in women (table 5), dyslipidemia/diabetes/hypertension are all still higher in the ‘High Stability’ group despite adjustment for weight in 2009?

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

PLoS One. 2021 May 13;16(5):e0251486. doi: 10.1371/journal.pone.0251486.r004

Author response to Decision Letter 1

4 Apr 2021

Response to the Reviewer’s Comments

Dear editor and reviewer,

The modified text is highlighted in tracked copy in the revised manuscript.

Question 1:

Introduction:

Line 45/46: “Millions of deaths can be ascribed to obesity worldwide [3], because it is associated with the development of cardiometabolic risk factors, such as dyslipidemia, diabetes, and hypertension[4, 5].”

This is not quite accurate. Dyslipidaemia, T2DM and hypertension are states of cardiometabolic disruption which increase the risk of adverse cardiovascular events.

Answer 1：Thank you for the question. we have revised as suggested. Please see line 45, page 3.

Question 2:

Line 46/47: “Fat mass is used to evaluate obesity[6]”

In ref #6 they defined obesity using a BMI cut-off, so I don’t think this statement makes sense.

Answer 2：Thank you for the question. we have revised as suggested. Please see line 47, page 3.

Question 3:

Line 61/62: “To date, few studies have characterized the trajectories of body composition and their associations with the development of cardiometabolic risk factors.”

Answer 3：Thank you for the question. we have revised as suggested.

Please see line 62 to 66, page 3 to 4.

Question 4:

Methods:

(Q5&6) It is still not clear to me, from the written description and from Figure 1, how the authors arrived at the final sample size. What are the numbers in the box at the top right corner of fig 1?

Answer 4：Thank you for the question, we have revised as suggested. Please see figure 1.

We have revised figure 1 and make it clearer. In this study, we included 6 waves from 1993 to 2009 of the cohort. Participants we chose based on the wave 6 in 2009 which had blood biochemical results. There were 104, 63, 2, 62 new participants entered into the cohort in 1997, 2000, 2004 and 2006, respectively. The final sample size was 3013, which comprised of 2782 entered in 1993 and 231 new entered in 1997 to 2006.

Question 5:

Answer 5：Thank you for the question.

The formation of trajectory trend was based on all the data involved in the analysis. The procedure of model fitting was as: Firstly, when all the data was imported in the Mplus, linear models and quadratic models were fitted. It showed that quadratic models had much smaller Bayesian information criteria (BIC) values across all models. Therefore, further analyses were based on the quadratic curve assumption and 4 classes in men, 3 classes in women. After the models and classes were determined, everyone was classified into a trajectory class.

To maximize the use of the data, we include 2 measurements in this study as other researchers did in theirs studies. In this study, 2 measurements of F2FFMR was only 4.8% (145/3013). After we exclude the data of 2 measurements, there was no obvious change in Figure 2.

1. Body mass index trajectories during the first year of life and their determining factors. American Journal of Human Biology. DOI: 10.1002/ajhb.23188

2. Body mass index trajectories during infancy and pediatric obesity at six years. Annals of Epidemiology.10.1016/j.annepidem.2017.10.008

Question 6:

When were the cardiometabolic outcomes measured (at which visit)?

Was waist-hip ratio measured in this study?

Answer 6：Thank you for the question, we have revised as suggested. The cardiometabolic outcomes were measured in wave 6 in 2009.There was no hip measure in this study. Please see page 6.

Question 7:

(Q8) This is not true. Type 1 Diabetes develops in childhood but is still present into adulthood.

Answer 7：Thank you for the information. I did some literatures review and found that China is one of the countries with the lowest incidence of type 1 diabetes. Although type 1 diabetes tends to develop in children, most of the new cases are diagnosed in adults.

Weng J, Zhou Z, Guo L, Zhu D, Ji L, Luo X, Mu Y, Jia W; T1D China Study Group. Incidence of type 1 diabetes in China, 2010-13: population based study. BMJ. 2018 Jan 3;360:j5295. doi: 10.1136/bmj.j5295.

In this study, the data was unavailable, we could not distinguish between type 1 diabetes and type 2 diabetes. There was no population-based data about the prevalence of type 1 diabetes in China, and it was 0.5% in US. So, the majority of diabetes in our study were T2DM. There might be a slight impact on our results.

Xu G, Liu B, Sun Y, Du Y, Snetselaar LG, Hu FB, Bao W. Prevalence of diagnosed type 1 and type 2 diabetes among US adults in 2016 and 2017: population based study.BMJ.2018,362:k1497.doi: 10.1136/bmj.k1497.

Question 8:

Results:

Table 5, statistically significant ORs not highlighted bold, as was done in Table 4.

Answer 8：Thank you for the question, we have revised as suggested. Please see page 14 and 15.

Question 9:

Discussion:

Lines 247-250: “The effect of F2FFMR on cardiometabolic risk is likely to accumulate slowly, and high F2FFMR in early life might have an impact on cardiometabolic health in later life.”

It is not clear if these statements are based on evidence presented in previously published work? Or are speculation.

Answer 9: Thank you for the question, we have revised as suggested. Please line 252 to 255, page 16.

Question 10:

Line 256/256: “Study of the F2FFMR indicates that the adverse effects of high fat mass can be offset by the protective effects of high fat-free mass.”

Line 267/268: “The present findings also suggest that the impact of F2FFMR trajectory on cardiometabolic outcomes is mediated by changes in body mass.”

Is it ‘changes in body mass’ or just ‘current weight’? I think you should revise your interpretation of your results.

Answer 10: Thank you for the question, we have revised as suggested. It should be current body mass.

Question 11:

Line 270: do you mean the ‘high stability’ group or do you mean ‘highly stable’?

Answer 11: Thank you for the question, we have revised as suggested. Please see page 17.

Question 12:

Answer 12: Thank you for your advice. We added some points in it.

Question 13:

Line299/300: “Our results also suggest that the association between F2FFMR trajectory and the cardiometabolic profile is mediated by body mass.”

Answer 13: Thank you for the question, we have revised as suggested. Please see page 18.

Attachment

Submitted filename: Response to the reviewer.docx

Click here for additional data file.^{(21.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0251486.r005

Decision Letter 2

Y Zhan

28 Apr 2021

Long-term Changes in Body Composition and their Relationships with Cardiometabolic Risk Factors: A Population-Based Cohort Study

PONE-D-20-38516R2

Dear Dr. Liu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Y Zhan

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: I Don't Know

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: No

**********

6. Review Comments to the Author

Reviewer #2: The authors have addressed the comments sufficiently. The data is interesting and, to the best of my knowledge, the analysis is appropriate. English language used has been vastly improved however there are still small errors and places where the writing does not flow well. It is at the editors discretion whether these should be addressed.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

PLoS One. doi: 10.1371/journal.pone.0251486.r006

Acceptance letter

Y Zhan

30 Apr 2021

PONE-D-20-38516R2

Long-term Changes in Body Composition and their Relationships with Cardiometabolic Risk Factors: A Population-Based Cohort Study

Dear Dr. Liu:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Y Zhan

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Attachment

Submitted filename: Response to the Reviewers.docx

Click here for additional data file.^{(20.8KB, docx)}

Attachment

Submitted filename: Response to the reviewer.docx

Click here for additional data file.^{(21.5KB, docx)}

Data Availability Statement

The datasets analyzed during the current study are available in the following website: http://www.cpc.unc.edu/projects/china/.

[pone.0251486.ref001] 1.Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9·1 million participants. Lancet. 2011; 377: 557–567. 10.1016/S0140-6736(10)62037-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref002] 2.Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014; 384: 766–781. 10.1016/S0140-6736(14)60460-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref003] 3.Wormser D, Kaptoge S, Di Angelantonio E, Wood AM, Pennells L, Thompson A et al. Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies. Lancet. 2011; 377: 1085–1095. 10.1016/S0140-6736(11)60105-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref004] 4.Berrington de Gonzalez A, Hartge P, Cerhan JR, Flint AJ, Hannan L, MacInnis RJ et al. Body-mass index and mortality among 1.46 million white adults. N Engl J Med. 2010; 363: 2211–2219. 10.1056/NEJMoa1000367 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref005] 5.Ward ZJ, Long MW, Resch SC, Giles CM, Cradock AL, Gortmaker SL. Simulation of Growth Trajectories of Childhood Obesity into Adulthood. N Engl J Med. 2017; 377: 2145–2153. 10.1056/NEJMoa1703860 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref006] 6.Fan H, Zhang X. Body mass index trajectory across childhood and subsequent risk of elevated blood pressure. J Clin Hypertens (Greenwich). 2020; 22: 1902–1907. 10.1111/jch.14001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref007] 7.Huang F, Zhang M, Li Y, Li Z, Fang J, Guo K. The longitudinal trajectory of body mass index in the Chinese population: A latent growth curve analysis. PLoS One. 2018; 13: e0207845. 10.1371/journal.pone.0207845 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref008] 8.Liu J, Frongillo EA, Boghossian NS, Cai B, Zhou H, Hazlett LJ. Body mass index trajectories during the first year of life and their determining factors. American journal of human biology: the official journal of the Human Biology Council. 2019; 31. 10.1002/ajhb.23188 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref009] 9.Liu JX, Liu JH, Frongillo EA, Boghossian NS, Cai B, Hazlett LJ. Body mass index trajectories during infancy and pediatric obesity at 6 years. Ann Epidemiol. 2017; 27: 708–715.e1. 10.1016/j.annepidem.2017.10.008 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref010] 10.Mattsson M, Maher GM, Boland F, Fitzgerald AP, Murray DM, Biesma R. Group-based trajectory modelling for BMI trajectories in childhood: A systematic review. Obes Rev. 2019; 20: 998–1015. 10.1111/obr.12842 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref011] 11.Brøns C, Grunnet LG. MECHANISMS IN ENDOCRINOLOGY: Skeletal muscle lipotoxicity in insulin resistance and type 2 diabetes: a causal mechanism or an innocent bystander? Eur J Endocrinol. 2017; 176: R67–R78. 10.1530/EJE-16-0488 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref012] 12.Markus MRP, Stritzke J, Siewert U, Lieb W, Luchner A, Döring A et al. Variation in body composition determines long-term blood pressure changes in pre-hypertension: the MONICA/KORA (Monitoring Trends and Determinants on Cardiovascular Diseases/Cooperative Research in the Region of Augsburg) cohort study. J Am Coll Cardiol. 2010; 56: 65–76. 10.1016/j.jacc.2010.01.056 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref013] 13.Siervo M, Prado CM, Mire E, Broyles S, Wells JCK, Heymsfield S et al. Body composition indices of a load-capacity model: gender- and BMI-specific reference curves. Public Health Nutr. 2015; 18: 1245–1254. 10.1017/S1368980014001918 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref014] 14.Wells JCK. The capacity-load model of non-communicable disease risk: understanding the effects of child malnutrition, ethnicity and the social determinants of health. Eur J Clin Nutr. 2018; 72: 688–697. 10.1038/s41430-018-0142-x [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref015] 15.Wells JCK, Victora CG. Indices of whole-body and central adiposity for evaluating the metabolic load of obesity. Int J Obes (Lond). 2005; 29: 483–489. 10.1038/sj.ijo.0802899 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref016] 16.Powell M, Lara J, Mocciaro G, Prado CM, Battezzati A, Leone A et al. Association between ratio indexes of body composition phenotypes and metabolic risk in Italian adults. Clin Obes. 2016; 6: 365–375. 10.1111/cob.12165 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref017] 17.Jiang Y, Zhang Y, Jin M, Gu Z, Pei Y, Meng P. Aged-Related Changes in Body Composition and Association between Body Composition with Bone Mass Density by Body Mass Index in Chinese Han Men over 50-year-old. PLoS One. 2015; 10: e0130400. 10.1371/journal.pone.0130400 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref018] 18.Ho SC, Wu S, Chan SG, Sham A. Menopausal transition and changes of body composition: a prospective study in Chinese perimenopausal women. Int J Obes (Lond). 2010; 34: 1265–1274. 10.1038/ijo.2010.33 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref019] 19.Zhong R-H, Long J-A, Wang F, Chen S, Luo Y, Lu X-T et al. Association between serum choline and betaine concentrations and longitudinal changes of body composition in community-dwelling middle-aged and older Chinese adults. Appl Physiol Nutr Metab. 2020; 45: 737–744. 10.1139/apnm-2019-0778 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref020] 20.Popkin BM, Du S, Zhai F, Zhang B. Cohort Profile: The China Health and Nutrition Survey—monitoring and understanding socio-economic and health change in China, 1989–2011. Int J Epidemiol. 2010; 39: 1435–1440. 10.1093/ije/dyp322 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref021] 21.Yan S, Li J, Li S, Zhang B, Du S, Gordon-Larsen P et al. The expanding burden of cardiometabolic risk in China: the China Health and Nutrition Survey. Obes Rev. 2012; 13: 810–821. 10.1111/j.1467-789X.2012.01016.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref022] 22.Lean ME, Han TS, Deurenberg P. Predicting body composition by densitometry from simple anthropometric measurements. Am J Clin Nutr. 1996; 63: 4–14. 10.1093/ajcn/63.1.4 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref023] 23.Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003; 289: 2560–2572. 10.1001/jama.289.19.2560 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref024] 24.Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL et al. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension. 2003; 42: 1206–1252. 10.1161/01.HYP.0000107251.49515.c2 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref025] 25.Joint committee for guideline revision. 2016 Chinese guidelines for the management of dyslipidemia in adults. J Geriatr Cardiol. 2018; 15: 1–29. 10.11909/j.issn.1671-5411.2018.01.011 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref026] 26.Nagin D. Group-based modeling of development (Harvard University Press, Cambridge, Mass., London, 2005). [Google Scholar]

[pone.0251486.ref027] 27.Lennon H, Kelly S, Sperrin M, Buchan I, Cross AJ, Leitzmann M et al. Framework to construct and interpret latent class trajectory modelling. BMJ Open. 2018; 8: e020683. 10.1136/bmjopen-2017-020683 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref028] 28.Lemieux S, Prud’homme D, Bouchard C, Tremblay A, Després JP. Sex differences in the relation of visceral adipose tissue accumulation to total body fatness. Am J Clin Nutr. 1993; 58: 463–467. 10.1093/ajcn/58.4.463 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref029] 29.Power ML, Schulkin J. Sex differences in fat storage, fat metabolism, and the health risks from obesity: possible evolutionary origins. Br J Nutr. 2008; 99: 931–940. 10.1017/S0007114507853347 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref030] 30.Xiao P, Cheng H, Yan Y, Liu J, Zhao X, Li H et al. High BMI with Adequate Lean Mass Is Not Associated with Cardiometabolic Risk Factors in Children and Adolescents. J Nutr. 2020; 10.1093/jn/nxaa328 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref031] 31.Ito H, Nakasuga K, Ohshima A, Sakai Y, Maruyama T, Kaji Y et al. Excess accumulation of body fat is related to dyslipidemia in normal-weight subjects. Int J Obes Relat Metab Disord. 2004; 28: 242–247. 10.1038/sj.ijo.0802528 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref032] 32.Rothney MP, Catapano AL, Xia J, Wacker WK, Tidone C, Grigore L et al. Abdominal visceral fat measurement using dual-energy X-ray: association with cardiometabolic risk factors. Obesity (Silver Spring). 2013; 21: 1798–1802. 10.1002/oby.20223 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref033] 33.Tian Z, Li Y, Mao Z, Yu S, Wang Y, Liu X et al. Sex-specific relationship between visceral fat index and dyslipidemia in Chinese rural adults: The Henan Rural Cohort Study. Prev Med. 2018; 116: 104–111. 10.1016/j.ypmed.2018.09.009 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref034] 34.Park SK, Ryoo J-H, Oh C-M, Choi J-M, Chung P-W, Jung JY. Body fat percentage, obesity, and their relation to the incidental risk of hypertension. J Clin Hypertens (Greenwich). 2019; 21: 1496–1504. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref035] 35.Ittermann T, Werner N, Lieb W, Merz B, Nöthlings U, Kluttig A et al. Changes in fat mass and fat-free-mass are associated with incident hypertension in four population-based studies from Germany. Int J Cardiol. 2019; 274: 372–377. 10.1016/j.ijcard.2018.09.035 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref036] 36.Araújo J, Severo M, Barros H, Mishra GD, Guimarães JT, Ramos E. Developmental trajectories of adiposity from birth until early adulthood and association with cardiometabolic risk factors. Int J Obes (Lond). 2015; 39: 1443–1449. 10.1038/ijo.2015.128 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref037] 37.Gamboa-Gómez CI, Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The fat-to-lean mass ratio, a novel anthropometric index, is associated to glucose metabolic disorders. Eur J Intern Med. 2019; 63: 74–78. 10.1016/j.ejim.2019.03.017 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref038] 38.Jung SH, Ha KH, Kim DJ. Visceral Fat Mass Has Stronger Associations with Diabetes and Prediabetes than Other Anthropometric Obesity Indicators among Korean Adults. Yonsei Med J. 2016; 57: 674–680. 10.3349/ymj.2016.57.3.674 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref039] 39.Mizgier ML, Casas M, Contreras-Ferrat A, Llanos P, Galgani JE. Potential role of skeletal muscle glucose metabolism on the regulation of insulin secretion. Obes Rev. 2014; 15: 587–597. 10.1111/obr.12166 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref040] 40.Moore MC, Cherrington AD, Wasserman DH. Regulation of hepatic and peripheral glucose disposal. Best Pract Res Clin Endocrinol Metab. 2003; 17: 343–364. 10.1016/s1521-690x(03)00036-8 [DOI] [PubMed] [Google Scholar]

[pone.0251486.ref041] 41.Hong S, Chang Y, Jung H-S, Yun KE, Shin H, Ryu S. Relative muscle mass and the risk of incident type 2 diabetes: A cohort study. PLoS One. 2017; 12: e0188650. 10.1371/journal.pone.0188650 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0251486.ref042] 42.Son JW, Lee SS, Kim SR, Yoo SJ, Cha BY, Son HY et al. Low muscle mass and risk of type 2 diabetes in middle-aged and older adults: findings from the KoGES. Diabetologia. 2017; 60: 865–872. 10.1007/s00125-016-4196-9 [DOI] [PubMed] [Google Scholar]

PERMALINK

Long-term changes in body composition and their relationships with cardiometabolic risk factors: A population-based cohort study

Zhaoyang Fan

Yunping Shi

Guimin Huang

Dongqing Hou

Junting Liu

Roles

Abstract

1. Introduction

2. Methods

2.1. Study design

2.2. Study population

Fig 1. Flow chart of the derivation of the study cohort.

2.3. Measurement and definition of body fat mass

2.4. Cardiometabolic risk factors

2.5. Covariates and definitions

2.6. Statistical analysis

Table 1. Indices of goodness-of-fit for the latent class growth analysis.

Fig 2. Latent body fat trajectories of each group between 1993 and 2009, identified using group‐based trajectory modeling.

3. Results

Table 2. Characteristics of the male participants, according to their latent F2FFMR trajectory group.

Table 3. Characteristics of the female participants, according to their latent F2FFMR trajectory group.

Table 4. ORs and 95% CIs for the associations between the F2FFMR trajectories of men and cardiometabolic risk factors in 2009.

Table 5. ORs and 95% CIs for the associations between the F2FFMR trajectories of women and cardiometabolic risk factors in 2009.

4. Discussion

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Y Zhan

Roles

Author response to Decision Letter 0

Decision Letter 1

Y Zhan

Roles

Author response to Decision Letter 1

Decision Letter 2

Y Zhan

Roles

Acceptance letter

Y Zhan

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

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