Background The relationship between muscle and prognosis, especially that between muscle distribution across different body parts, and the related prognosis is not well established. Objective To investigate the relationship between muscle distribution and all-cause and cause-specific mortality and their potential modifiers. Design Longitudinal cohort study. C-index, IDI, and NRI were used to determine the best indicator of prognosis. COX regression analysis was performed to explore the relationship between variables and outcomes. Interaction and subgroup analyses were applied to identify the potential modifiers. Participants A total of 5052 participants (weighted: 124,841,420) extracted from the NHANES 2003–2006 of median age 45 years and constituting 50.3% men were assessed. For validation, we included 3040 patients from the INSCOC cohort in China. Main measures Muscle mass and distribution. Key Results COX regression analysis revealed that upper limbs (HR = 0.41, 95% CI 0.33–0.51), lower limbs (HR = 0.54, 95% CI 0.47–0.64), trunk (HR = 0.71, 95% CI, 0.59–0.85), gynoid (HR = 0.47, 95% CI 0.38–0.58), and total lean mass (HR = 0.55, 95% CI 0.45–0.66) were all associated with the better survival of participants (P trend < 0.001). The changes in the lean mass ratio of the upper and lower limbs and the lean mass ratio of the android and gynoid attenuated the protective effect of lean mass. Age and sex acted as potential modifiers, and the relationship between lean mass and the prognosis was more significant in men and middle-aged participants when compared to that in other age groups. Sensitive analyses depicted that despite lean mass having a long-term impact on prognosis (15 years), it has a more substantial effect on near-term survival (5 years). Conclusion Muscle mass and its distribution affect the prognosis with a more significant impact on the near-term than that on the long-term prognosis. Age and sex acted as vital modifiers.
BackgroundCentral obesity is closely related to comorbidity, while the relationship between fat accumulation pattern and abnormal distribution in different parts of the central region of obese people and comorbidity is not clear. This study aimed to explore the relationship between fat distribution in central region and comorbidity among obese participants.MethodsWe used observational data of NHANES 2011–2018 to identify 12 obesity-related comorbidities in 7 categories based on questionnaire responses from participants. Fat distribution is expressed by fat ratio, including Android, Gynoid, visceral, subcutaneous, visceral/subcutaneous (V/S), and total abdominal fat ratio. Logistic regression analysis were utilized to elucidate the association between fat distribution and comorbidity.ResultsThe comorbidity rate was about 54.1% among 4899 obese participants (weighted 60,180,984, 41.35 ± 11.16 years, 57.5% female). There were differences in fat distribution across the sexes and ages. Among men, Android fat ratio (OR, 4.21, 95% CI, 1.54–11.50, Ptrend=0.007), visceral fat ratio (OR, 2.16, 95% CI, 1.42–3.29, Ptrend<0.001) and V/S (OR, 2.07, 95% CI, 1.43–2.99, Ptrend<0.001) were independent risk factors for comorbidity. Among these, there was a “J” shape correlation between Android fat ratio and comorbidity risk, while visceral fat ratio and V/S exhibited linear relationships with comorbidity risk. The Gynoid fat ratio (OR, 0.87, 95%CI, 0.80–0.95, Ptrend=0.001) and subcutaneous fat ratio (OR, 0.81, 95%CI, 0.67–0.98, Ptrend=0.016) both performed a protective role in the risk of comorbidity. In women, Android fat ratio (OR, 4.65, 95% CI, 2.11–10.24, Ptrend=0.020), visceral fat ratio (OR, 1.83, 95% CI, 1.31–2.56, Ptrend=0.001), and V/S (OR, 1.80, 95% CI, 1.32–2.45, Ptrend=0.020) were also independent risk factors for comorbidity, with a dose-response relationship similar to that of men. Only the Gynoid fat ratio (OR, 0.93, 95% CI, 0.87–0.99, Ptrend=0.016) had a protective effect on female comorbidity. This association was also seen in obese participants of different age groups, comorbidity numbers, and comorbidity types, although it was more statistically significant in older, complex comorbidity, cardiovascular, cerebrovascular, and metabolic diseases.ConclusionsIn the obese population, there were strong correlation between fat distribution in central region and comorbidity, which was affected by sex, age, number of comorbidities, and type of comorbidity.
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