Overweight, obesity and risk of multimorbidity: A systematic review and meta‐analysis of longitudinal studies

Delpino, Felipe Mendes; Rodrigues, Ana Paula; Petarli, Glenda Blaser; Machado, Karla Pereira; Flores, Thaynã Ramos; Batista, Sandro Rodrigues; Nunes, Bruno Pereira

doi:10.1111/obr.13562

Cited by 19 publications

(11 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…

The missing piece: Why clinicians, epidemiologists, and policymakers need prediction intervals in a meta-analysis Delpino and colleagues have touched upon a very important topic in multiple long-term medical conditions-multimorbidity (MLTC-M). 1 Being overweight and having obesity leads to an elevated risk of developing MLTC-M (RR: 1.26, 1.12-1.40, I 2 of 98%; RR: 1.99, 1.45-2.72, I 2 of 99%) as reported by this systematic review and meta-analysis (SRMA).The authors have summarized the results from published studies.

…”

mentioning

confidence: 78%

See 1 more Smart Citation

The missing piece: Why clinicians, epidemiologists, and policymakers need prediction intervals in a meta‐analysis

Shamim,

Dwivedi,

Akhtar

et al. 2024

Obesity Reviews

View full text Add to dashboard Cite

show abstract

“…

…”

mentioning

confidence: 78%

“…2 of the study. 1 Since the analysis details were not given in a reproducible manner, we proceeded with the inverse variance method using the DerSimonian-Laird estimator for τ 2 . Our concerns were confirmed as the prediction intervals for the risk of MLTC-M crossed the risk ratio of one, both for obesity and overweight [0.81-4.72, 0.90-1.72, respectively].…”

mentioning

confidence: 99%

The missing piece: Why clinicians, epidemiologists, and policymakers need prediction intervals in a meta‐analysis

Shamim,

Dwivedi,

Akhtar

et al. 2024

Obesity Reviews

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

“…The association between adiposity and multimorbidity has been extensively explored in epidemiological studies [ 12 , 23 , 24 , 36 – 38 ]. For instance, a systematic review and meta-analysis of 8 longitudinal studies found a higher risk of multimorbidity among individuals with obesity [ 36 ]. Similarly, a cross-sectional study involving 20,198 participants from China, India, Ghana, Mexico, Russia and South Africa reported that significant positive association between obesity (measured by BMI and WC) and 5 out of 12 included chronic conditions [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Association of changes in waist circumference, waist-to-height ratio and weight-adjusted-waist index with multimorbidity among older Chinese adults: results from the Chinese longitudinal healthy longevity survey (CLHLS)

Chen,

Wang,

Zhong

et al. 2024

BMC Public Health

View full text Add to dashboard Cite

Background The association of changes in waist circumference (WC), waist-to-height ratio (WHtR) and weight-adjusted-waist index (WWI) with subsequent risk of multimorbidity remains unclear among older Chinese adults. Therefore, we aimed to assess this association by utilizing data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Methods Our study was based on the 2011/2012 wave of the CLHLS whose follow-up surveys were conducted in 2014 and 2017/2018. A total of 2900 participants aged 65 and above at baseline were enrolled. WC, WHtR, and WWI were calculated from measured height, weight, and waist circumference. Multimorbidity refers to the coexistence of two or more of 18 chronic diseases. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (95%CIs) to evaluate the effect of three-year changes in WC, WHtR, and WWI on the risk of multimorbidity. Results During a mean follow-up time of 4.2 (2.0) years, 906 multimorbidity cases were identified. Compared to participants in the persistently low WC group, those in the WC gain group and the persistently high WC group had a higher multimorbidity risk with adjusted HRs (95%CI) of 1.23 (1.01–1.50) and 1.34(1.14–1.58), respectively. Participants in the WHtR gain group and the persistently high WHtR group also had higher risks of multimorbidity with HRs (95%CI) of 1.35 (1.08–1.67) and 1.27 (1.05–1.53), respectively, relative to the persistently low WHtR group. Compared to the persistently low WWI group, those in the WWI loss group had a lower risk of multimorbidity with HRs (95%CI) of 0.80 (0.66–0.98). For every standard deviation increase in WC, WHtR, and WWI over three years, the risk of multimorbidity was higher by 12% (95%CI: 1.05–1.19), 13% (95%CI: 1.06–1.20), and 12% (95%CI: 1.05–1.20), respectively. Conclusions Associations of changes in WC, WHtR and WWI with multimorbidity are significant among older Chinese adults. The findings highlight the importance of evaluating changes in WC, WHtR, and WWI in screening and prevention of multimorbidity in older adults.

show abstract

“…Two recent systematic reviews focused on the prevalence of multimorbidity, 11 or analsed a single risk factor for multimorbidity development among subjects with overweight or obesity. 12 Other reviews generally focused on scoping older cross-sectional studies in one region (i.e., South Asia), 13 or solely considered non-communicable diseases (NCDs). 2,14 To our knowledge, none have focused on longitudinal investigations of determinants of multimorbidity consisting of both communicable and NCDs among the general populations in LMICs.…”

Section: Introductionmentioning

confidence: 99%

Determinants of multimorbidity in low‐ and middle‐income countries: A systematic review of longitudinal studies and discovery of evidence gaps

Tan,

Barbosa,

Pinho

et al. 2023

Obesity Reviews

View full text Add to dashboard Cite

SummaryMultimorbidity—the coexistence of at least two chronic health conditions within the same individual—is an important global health challenge. In high‐income countries (HICs), multimorbidity is dominated by non‐communicable diseases (NCDs); whereas, the situation may be different in low‐ and middle‐income countries (LMICs), where chronic communicable diseases remain prominent. The aim of this systematic review was to identify determinants (including risk and protective factors) and potential mechanisms underlying multimorbidity from published longitudinal studies across diverse population‐based or community‐dwelling populations in LMICs. We systematically searched three electronic databases (Medline, Embase, and Global Health) using pre‐defined search terms and selection criteria, complemented by hand‐searching. All titles, abstracts, and full texts were independently screened by two reviewers from a pool of four researchers. Data extraction and reporting were according to Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. Methodological quality and risk of bias assessment was performed using the Newcastle‐Ottawa Scale for cohort studies. Data were summarized using narrative synthesis. The search yielded 1782 records. Of the 52 full‐text articles included for review, 8 longitudinal population‐based studies were included for final data synthesis. Almost all studies were conducted in Asia, with only one from South America and none from Africa. All studies were published in the last decade, with half published in the year 2021. The definitions used for multimorbidity were heterogeneous, including 3–16 chronic conditions per study. The leading chronic conditions were heart disease, stroke, and diabetes, and there was a lack of consideration of mental health conditions (MHCs), infectious diseases, and undernutrition. Prospectively evaluated determinants included socio‐economic status, markers of social inequities, childhood adversity, lifestyle behaviors, obesity, dyslipidemia, and disability. This review revealed a paucity of evidence from LMICs and a geographical bias in the distribution of multimorbidity research. Longitudinal research into epidemiological aspects of multimorbidity is warranted to build up scientific evidence in regions beyond Asia. Such evidence can provide a detailed picture of disease development, with important implications for community, clinical, and interventions in LMICs. The heterogeneity in study designs, exposures, outcomes, and statistical methods observed in the present review calls for greater methodological standardisation while conducting epidemiological studies on multimorbidity. The limited evidence for MHCs, infectious diseases, and undernutrition as components of multimorbidity calls for a more comprehensive definition of multimorbidity globally.

show abstract

Overweight, obesity and risk of multimorbidity: A systematic review and meta‐analysis of longitudinal studies

Cited by 19 publications

References 43 publications

The missing piece: Why clinicians, epidemiologists, and policymakers need prediction intervals in a meta‐analysis

The missing piece: Why clinicians, epidemiologists, and policymakers need prediction intervals in a meta‐analysis

Association of changes in waist circumference, waist-to-height ratio and weight-adjusted-waist index with multimorbidity among older Chinese adults: results from the Chinese longitudinal healthy longevity survey (CLHLS)

Determinants of multimorbidity in low‐ and middle‐income countries: A systematic review of longitudinal studies and discovery of evidence gaps

Contact Info

Product

Resources

About