Association of Percentage Body Fat and Metabolic Health in Offspring of Patients with Cardiovascular Diseases

Family history was one of the major risk factors for developing adverse health outcomes such as metabolic syndrome (MetS), type 2 diabetes mellitus (DM) and hypertension (HTN). Our aim was to examine the relationship between different family histories and cardiometabolic events, including DM, stroke, myocardial infarction (MI), and HTN. Participants who attended the health examinations at the Tri-Service General Hospital from 2010 to 2016 were enrolled in the study and were categorized into four groups by representing different family history. A multivariable logistic regression model was used for the associations between various family history with the cardiometabolic events. Subjects with family history of DM were divided into quartiles by percentage body fat (PBF) to be analyzed for these adverse outcomes. In the cross-sectional analysis, subjects with family history of DM had significant association with MetS (OR = 1.34 [95%CI: 1.17–1.54]) and DM (OR = 3.03 [95%CI: 2.44–3.76]), and those with family history of HTN were positively associated with HTN (OR = 1.60 [95%CI: 1.41–1.81]). Notably, those with family history of DM in higher PBF quartiles had substantially increased association of cardiometabolic events (MetS: OR = 15.20 [95%CI: 9.87–23.39]; DM: OR = 3.35 [95%CI: 1.91–5.90]; HTN: 2.81 [95%CI: 1.84–4.29]). Individuals with family history of DM were positively associated with MetS and DM, and those with family history of HTN was associated with HTN. Family history assessment was requested especially in obese population for screening adverse health outcomes.

a family history of DM had an increased risk of DM and MetS 11 . The incidence of DM was increased among individuals with a family history of DM in Caucasians 12 .
Emerging studies reported that increased adiposity was associated with risks of cardiometabolic diseases 13,14 . High percentage body fat (PBF) was correlated with increased risk of DM even having a normal body mass index (BMI) 15 . In the present study, we hypothesized that participants who had family history with high PBF would have more closely association with cardiometabolic events than those with low PBF. Moreover, we examined the associations between combinations of different family histories and cardiometabolic risks. Our main goal was to ascertain the relevance and usefulness of PBF in the relationship between family histories with the cardiometabolic events.

Results
Demographic characteristics of the study population. There were 13561 participants with family history of DM, 3775 with family history of MI, 5460 with family history of stroke, and 18399 with family history of HTN. The mean age of each subgroup was 40.68 ± 13.58 (DM), 42.10 ± 13.92 (MI), 42.56 ± 14.18 (stroke), and 42.69 ± 14.37 (HTN). All characteristics of participants in different family history subgroups were shown in Table 1. Table 2, a multivariable logistic regression analysis was performed for the association between various family history and MetS, DM, and HTN. The odds ratios (ORs) for MetS in subjects with family history of DM were 1. 31  Association between various combinations of family history and DM. In Table 4 (Table 5).

Discussion
In the current study, we observed that those with family history of DM had significant association with MetS and DM, and those with family history of HTN were positively associated with HTN. Subjects with family history of DM in higher PBF quartiles were associated with these cardiometabolic events. In addition, family history combinations which contained family history of DM had significant association with the presence of DM. Subjects who had family history of DM with different cut-off values of PBF could predict the risks of MetS, DM, and HTN. Our finding was the first to examine the association between various family history and cardiometabolic events in Taiwanese general population. Previous researches had reported the important role of not only family history of HTN but also family history of DM in predicting the risks of different adverse health outcomes. In a long-term prospective study, both paternal and maternal hypertension were significantly associated with higher blood pressure and with the development of HTN over the adult life course 16   risks of cardiometabolic disorders such as MetS, DM and cardiovascular diseases 6,18,19 . In a Korean study composed of a young adult population, those with family history of DM had increased risks of MetS and DM 11 . These results were consistent with our findings that the prevalence of MetS and DM was greater in subjects with family history of DM, and those with family history of HTN were correlated with the presence of HTN. Several studies had addressed the synergistic effect of both positive family history of DM and obesity on the risk of developing DM. Hilding et al. demonstrated that exposure to both family history of DM and BMI showed the strongest effect on the risk of developing pre-diabetes and DM and conveyed a higher risk than either alone 20 . In an American study composed of a Indians population, family history of DM was correlated with higher incidence of DM with increased BMI than in those without family history of DM 21 . Obesity had been indicated to be associated with family history of DM and might be substantial part of the association between family history and the risk of DM 22,23 . In a Japanese cohort study, a family history of DM was associated with the incident risk of DM, and this association was independent of interactions with obesity and lifestyle factors 24 . Generalized adiposity reflected by BMI contributed to the association between family history of DM and the risk of developing DM 25 . A similar finding observed by Rice et al. represented that total body fat shared common familial determinants 26 . Consistent with our findings, combined family history of DM and higher PBF had increased risk for predicting MetS, DM and HTN than those in lower PBF quartiles. It was tempting to speculate that existing family history increased cardiometabolic risks and ensuing higher PBF could result in metabolic derangement and endothelial dysfunction that harbored a predisposing milieu for cardiometabolic diseases.
Several limitations were noted among the current study. First, a causal inference was not suitable in the study due to a cross-sectional design. A longitudinal analysis was necessary for further researches to examine the association between family history and the risks of adverse health outcomes. Second, the results might be influenced by recall bias because self-reported data was performed in the study to categorize family history which could result in some misclassification. Thus, we validated the family history collected by the questionnaire to eliminate inaccurate information. Third, the study sample was obtained from adult population in Taiwan. The limited ethnicity distribution of participants might not present the effect of family history on adverse health outcomes in terms of racial differences. Next, we only recruited study sample from a single hospital, which could limit generalization to the Taiwanese general population.   Last, detail family histories in father and mother and familial risk classification were not recorded. It cannot be provided because our dataset included no information on detail family histories in first-degree relative or second degree relative. Familial risk classifications were not categorized in our analyzed models.

Conclusion
By assessing the Taiwanese adult population, the current study highlighted that family history of DM was significantly associated with cardiometabolic events. Distinctly, body fat accumulation obviously contributed to increased risks of MetS, DM and HTN, especially in offspring of patients with DM. Prevention strategies of cardiovascular diseases would benefit from giving more attention to lower body fat percentage in individuals with family history. The potential role of reducing PBF in the prevention of cardiometabolic diseases warranted more longitudinal surveys to explore the clinical applications.

Methods
Subjects Enrollment. Subjects enrolled in the study were derived from the health examinations at TSGH, from 2010 to 2016. Participants aged 20 years old and older attended comprehensive examinations including laboratory data, body composition and detailed self-reported questionnaires. All protocols in this retrospective study were approved by the Institutional Review Board (IRB) of TSGH. The IRB waived the need to obtain individual informed consent because the data were analyzed anonymously. All methods were performed in accordance with the relevant guidelines and regulations of TSGH.

Study Design.
According to the flowchart shown in Fig. 1  Family History Assessment. Family history was obtained from participants who attended the health examinations at TSGH by using a detailed self-reported questionnaire. A question "What family history do you have" was used for assessing various kinds of family history. Definition of HTN. The definition of HTN in the current study was adopted by the guidelines which blood pressure ≥ 140/90 mmHg or subjects taking antihypertensive agents 32 .

Definition of MetS.
Measurement of Body Composition. PBF was measured by bioelectrical impedance analysis (BIA) (InBody720, Biospace, Inc., Cerritos, CA, USA), an useful method for assessing body composition 33 . The procedure of BIA was simple and noninvasive, and the results were reproducible and rapidly obtained.

Assessments of Covariates.
These pertinent characteristics included demographic factors (age, gender), biochemistry data (body mass index (BMI), proteinuria, serum total cholesterol, uric acid, creatinine, aspartate aminotransferase (AST), albumin, highly sensitive C-reactive protein (hsCRP)), and personal history (cigarette smoking, alcoholic consumption). A self-reported questionnaire was used to obtain age, gender and personal history. BMI was calculated by a formula that the weight divided by the square of the height (kg/m 2 ) of a participant.
Subjects were asked to fast at least 8 hours before health examinations for collecting blood samples. Biochemistry data was analyzed by different standard measurements. Total cholesterol was analyzed by an enzymatic colorimetric method (Roche Diagnostics, Indianapolis, IN, USA). The latex-enhanced nephelometry was used to detect hsCRP. Serum uric acid was measured by the Hitachi 737 automated multichannel chemistry analyzer (Boehringer Mannheim Diagnostics, Indianapolis, IN, USA).
Statistical Analysis. We classified subjects into different family history and compared the distribution of characteristics and covariates across subgroups by using ANOVA for continuous variables and the chi-squared test for categoric variables. Statistical significance was defined as a two-sided p-value of ≤0.05. Multivariable models were adjusted as follows: Model 1 included age, gender and BMI. Model 2 included Model 1 plus proteinuria, serum total cholesterol, uric acid, creatinine, AST, albumin and hsCRP. Model 3 included Model 2 plus history of cigarette smoking and alcoholic consumption. A logistic regression model was investigated for the association between family history and the risk of developing MetS, DM and HTN. Analyses in the current study were conducted by Statistical Package for the Social Sciences, version18.0 (SPSS Inc., Chicago, IL, USA) for Windows.