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Impact of adiposity indices changes across the lifespan on risk of diabetes in women: trajectory modeling approach

樱花视频 volume听24, Article听number:听2429 (2024) Cite this article

Abstract

Aims

The impact of life-course different adiposity indices on diabetes mellitus (DM) is poorly understood. We aimed to do trajectory analysis with repeated measurements of adiposity indices in the development of DM among women across the lifespan.

Methods

This study prospectively investigated the 1,681 population of Tehran Lipid and Glucose Study. At baseline, all individuals were free of diabetes. Trajectory analysis was used to identify homogeneous distinct clusters of adiposity indices trajectories and assign individuals to unique clusters.

Results

Of the 1681 healthy women, 320 progressed to the DM. Three distinct body mass index (BMI) trajectories and 2 distinct trajectories of other adiposity indices (waist circumstance (WC), conicity index (C-index), and body roundness index (BRI)) were chosen as the best fitting of the latent class growth mixture model. In the adjusted model, total participants [HR (CI 95%): 2.83 (2.05, 3.91); p鈥�<鈥�0.001], and menopause [1.35 (1.10, 2.11); p鈥�=鈥�0.001] and reproductive-age women [2.93 (1.80, 4.78); p鈥�=鈥�0.003] of the high BMI trajectory compared to the ones in the low trajectory of BMI were more likely to develop DM. The high BRI in menopause had a significantly higher risk of DM compared to the low trajectory. In menopause (1.80 (1.26, 2.58)) and reproductive-age women (4.32 (2.49, 7.47)) with high trajectory of C-index, the DM risk decreased after adjustment.

Conclusions

The risk of DM was greater for high BMI, WC, C-index, and BRI trajectories than for lower trajectories. Hence, the development of general, abdominal, and visceral obesity trajectories in the prevention of DM should be considered by clinicians.

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Introduction

Diabetes Mellitus (DM) is a widespread global disease that threatens the well-being of individuals [1]. Recently, the global burden of diabetes has been on a rising trend, with over 10.5% of the world鈥檚 adults suffering from DM [2, 3]. Although governments invest a lot in research and clinical care for DM, the global burden of it is on a rising tide [4], which has doubled in low and middle-income countries [5]. Factors like lifestyle, socioeconomic deprivation, and air pollution are considered environmental drivers of DM [6]. Biological factors like body mass index (BMI), body fat distribution, brown adipose tissue, metabolic syndrome (MetS), imbalance of sex hormones, and health behaviors, including smoking and alcohol consumption, have also been considered the common risk factors for DM [7].

Today, obesity has reached pandemic levels and become a major health challenge worldwide [8]. Adiposity has posed a significant economic burden on developed and developing countries [9]. Adipose tissue is well-known for its impact on the metabolic and cardiovascular systems by influencing endocrine and paracrine pathways [10, 11]. Different indicators are used to estimate the obesity rate. Anthropometric indicators like BMI, waist circumference (WC), Conicity Index (C-index), and Body Roundness Index (BRI), have become useful tools for evaluating obesity [12]. A study showed that WC, waist-to-hip ratio (WHR), and waist- to-height ratio (WHtR) are better than BMI for detecting DM [13]. According to another study among the Chinese population, WtHR and C-index had the best ability to distinguish DM [14]. Maessen et al. studied the Netherlands population and found that BRI had the same ability to identify cardiovascular disease risk factors as BMI and WC [15].

Obesity could be generally divided into three categories: general obesity, central, and visceral adiposity. BMI is the commonly used measurement for general obesity, while WC and C-index are used to measure central obesity. The evaluation of visceral obesity involves the widespread use of the alternative variable BRI [16]. Abdominal obesity is known as a major predictor of cardio-metabolic risk [17]. Despite the excessive risk of cardio-metabolic disturbances among obese individuals, there are some phenotypes of obesity in which individuals experience metabolically healthy obesity [18]. It is proposed that visceral adiposity is linked to insulin resistance via the release of non-esterified fatty acids from visceral fat into the portal vein and a high rate of lipolysis [19]. Menopausal status in women can increase the risk of abdominal and visceral obesity [20]. The menopause-related adiposity plus androgenicity and psychological issues in postmenopausal women can also increase the risk of DM [21]. However, some studies support a stronger association between obesity and DM in premenopausal women than in postmenopausal ones [22].

To date, many studies have investigated the risk factors for developing DM [23,24,25,26]. However, the majority of previous studies did not consider the association of changing pattern of these variables with incident of DM [27,28,29]. Moreover, the majority of previous studies used the traditional method for looking at the overall weight change by the time, and not considered its variations throughout their life [30, 31]. Trajectory modeling enable us to consider all of the variation and identify individuals with similar progression over time. It determines individuals with similar risk factor patterns that can improve risk stratification [32, 33]. Among the various trajectory approaches, the latent class model has the advantage of identifying the latent subpopulations within the data; this model not only captures the heterogeneity of individual trajectories but also elucidates the underlying structures that may influence the outcomes [34]. Likewise, less is known about the impact of adiposity indices on the risk of DM among menopause and reproductive age groups. Therefore, the present study aimed to apply the latent class trajectory models to identify distinct trajectories of adiposity indices and their association with risk of DM among women participants of a population-based study of Tehran Lipid and Glucose Study (TLGS). Furthermore, this study aimed to investigate whether this association differed among individuals according to their menopausal status.

Method

Study design and subjects

We used data from the TLGS, a prospective, population-based study, initiated in 1998 and aimed at assessing the prevalence and determinants risk factors for non-communicable diseases. This cohort study recruited 15,000 participants (both male and female) from an urban population aged 3鈥�70 years. Demographics, reproductive variables, and various risk factors for non-communicable diseases have been collected at baseline and in consecutive 3-year follow-up sessions. The methodology of the TLGS study has been reported elsewhere [35]. For the purposes of the present study, we selected female participants aged over 20 who met our eligibility criteria, which included the availability of the required variables for all seven visits (baseline and six follow-up visits) and the absence of diabetes at the initiation of the study (n鈥�=鈥�6,840). We further excluded participants who had missing information regarding adiposity indices or diabetes status throughout the follow-up period. This resulted in a final total of 1,681 women remaining for analysis (Fig.听1).

Fig. 1
figure 1

Flowchart of study

Full size image

Data collection

A trained staff collected related data through standard protocol and a general practitioner assessed the anthropometric and physical examinations [35, 36]. A blood sample was obtained from all participants after a 12- to 14-hour overnight fast (between 7:00 and 9:00 a.m.) for assessment of fasting blood glucose, at each visit [35]. For those who were not taking medication to lower plasma glucose after a 2-hour post-challenge, they consumed 82.5听g of glucose monohydrate solution and a blood sample was taken 2听h later. All measurements were performed at baseline and at each follow-up according to the standard protocol of TLGS, which has been addressed elsewhere [35] .We used the following adiposity indices: BMI, C-index, WC, and BRI. Weight and height measurements were performed using a digital scale with an accuracy of 100听g and a stadiometer with an accuracy of nearest 0.5听cm was used for measuring height. Waist circumference was measured at the level midway between the lower rib margin and the iliac crest with participants in a standing position.

Definitions

Body mass index (BMI) was calculated by dividing your weight in kilograms by square of height (meters). The conicity index (C-index) and body roundness index (BRI) are proposed by the following formulas [37, 38]:

C-index鈥�=鈥塛C (m)/ [0.109 X鈭� {Bodyweight (kg)/ Height (m)]

$$\:\text{B}\text{R}\text{I}=364.2-365.5\times\:1-\surd\:\:\left(\right(\text{W}\text{C}/\left(2\pi\:\right)\left)2\right(0.5\text{h}\text{e}\text{i}\text{g}\text{h}\text{t}\left)2\right)$$

Participants were asked about their physical activity by using a modifiable activity questionnaire [39, 40]. Variable of smoking status was defined as ever smoker or never smoker. DM was defined as fasting blood sugar鈥夆墺鈥�126听mg/dL or 2-hour plasma glucose concentrations鈥夆墺鈥�200听mg/dL, or treatment with antidiabetic medications [41].

Menopause was defined as the permanent cessation of menstrual bleeding for at least 12 months [42].

Ethical considerations

The ethics committee of the Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences approved the study protocol. All participants signed the informed written consent form. The study was performed in adherence to the Declaration of Helsinki.

Statistical analysis

Descriptive parameters for population characteristics are reported as median (IQR), and categorical data are expressed as numbers and percentages. To determine the normality assumption, Kolmogorov鈥揝mirnov test was used. The baseline characteristics were compared between the diabetes and non-diabetes groups at follow-ups, by applying the student t-test or Man-Whitney U test for continuous variables or 蠂2 test for categorical data, respectively.

Trajectory models were used to obtain homogeneous distinct clusters of adiposity indices: BMI, C-index, WC, and BRI, following similar progressions over time, assign individuals to unique clusters within a study population, estimate the proportion of participants in each group and their probability of membership to that group over time [43].

Obtained trajectories indicate the presence of unobserved patient subgroups within the population, characterized by varying treatment responses and heterogeneous health phenotypes. To identify the adiposity trajectories, we included only those participants with complete data on adiposity indices for all follow-up visits (7 time points) irrespective of experiencing the outcome (DM). A plot of the average trajectory was used to summarize the overall change over time for each cluster. To investigate the association between the trajectory group membership and incident diabetes, we used Cox proportional hazard models, with the unadjusted model (Model 1) and an adjusted model for baseline age, physical activity, smoking status, and family history of diabetes (Model 2). Additionally, sub-group analyses were carried out for women who had stable menopause status (n鈥�=鈥�568) and reproductive age (n鈥�=鈥�364) from baseline until the outcome emerged. Sub-group analysis excludes women whose reproductive status changes to menopause during the study period.

The optimal number of clusters has to make a 鈥済ood鈥� partition means a partition where clusters are firstly compact and secondly, well separated from each other. In Longitudinal k-means clustering method, this optimal value was determined using the Calinski and Harabatz [44], the Ray and Turi [45] and Davies & Bouldin [46] criteria. All statistical analysis was performed using 鈥榮urvival鈥� and 鈥榢ml鈥� packages of R statistical software (version 3.4.3). The significant level was set at P-value鈥�<鈥�0.05, and CI as 95%.

Results

A total of 1,681 participants were eligible to enter into this study. The study flowchart is presented in Fig.听1. Out of 1,681 women who participated in the present study, a total number of 320 (19.03%) women experienced DM during a median follow-up time of 16 years (IQR: 15鈥�17).

Table听1 presents the baseline characteristics of the participants based on diabetes experience until the last follow -up. There was a significant difference in the median age (44 vs. 38 years) among women who experienced diabetes and those one non-experienced. Furthermore, all adiposity indices including BMI, WC, BRI and C-index among diabetic women were significantly higher compared to those without diabetes (p鈥�<鈥�0.05). There were no differences with respect to physical activity, and smoking history (p鈥�>鈥�0.05). There were significant differences among diabetic and non-diabetic menopausal women in terms of baseline BRI, C-index, WC, and number of parity (p鈥�<鈥�0.05). However, there were significant differences among diabetic women in reproductive age women compared to non-diabetics in terms of age, BMI, WC, BRI, C-index, physical activity and family history of DM (p鈥�<鈥�0.05) (Table听2).

Table 1 Baseline characteristics of the participants based on diabetes experience until last follow up
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Table 2 Baseline characteristics of the participants based on menopausal status
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Figure听2 shows the longitudinal trajectories of adiposity indices during six follow-ups for all 1681 participants. As shown based the best fitting of latent class growth mixture trajectory model revealed three trajectories for BMI [low (36.5%, n鈥�=鈥�614), medium (44.3%, n鈥�=鈥�745), and high (19.2%, n鈥�=鈥�322)], two trajectories for C-index [low (53.5%, n鈥�=鈥�900), high (46.5%, n鈥�=鈥�781)], two trajectories for WC [low (48.8%, n鈥�=鈥�821), high ( 51.2%, n鈥�=鈥�860)] and two trajectories for BRI [low (39.6%, n鈥�=鈥�666), and high (60.4%, n鈥�=鈥�1015)]. Table听3 shows statistical description of observed different trajectories of adiposity variables over follow ups.

Fig. 2
figure 2

Predicted trajectories of body mass index (BMI), waist circumference (WC), conicity index (C-index) and body roundness index (BRI) during follow-ups. The trajectories are shown in solid line and capital letters. The proportions in each trajectory are shown above the graphs

BMI graph; A: Medium, B: Low, and C: High trajectory. C-index, WC and BRI graphs; A: Low, and B: High trajectory

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Table 3 Statistical description of adiposity variables in different trajectories over follow ups
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Table听4 represents the association between the trajectories of adiposity indices and DM in unadjusted and adjusted Cox proportional hazard models. According to the adjusted model, women with high BMI trajectory were about three times more than ones in the low trajectory of BMI were at risk of developing DM [HR (CI 95%): 2.83 (2.05,3.91); p鈥�&濒迟;鈥�0.001闭. Also, participants with menopause status with high BMI trajectory were 35% more likely to develop DM [HR (CI 95%): 1.35 (1.10, 2.11); p-value鈥�=鈥�0.001] and women in reproductive age were 2.93 times [HR (CI 95%): 2.93 (1.80, 4.78); p鈥�=鈥�0.003] more likely to develop DM compared to ones in the low trajectory of BMI. Moreover women in the medium class trajectory of BMI compared to the low class, were 77% more likely to develop DM in total subjects [HR (CI 95%): 1.77 (1.31,2.40);p鈥�<鈥�0.001], 25% more likely in menopausal women [HR (CI 95%): 1.25 (1.02,1.88); p鈥�=鈥�0.001] and 75% more likely in reproductive age women [HR (CI 95%):1.75 (1.11,2.77); p鈥�=鈥�0.016] in adjusted models.

Table 4 HRs and 95% confidence intervals of BMI, waist, C-index and BRI trajectory groups for incidence DM
Full size table

Moreover, the high BRI trajectory compared to the low trajectory in total subjects intensified the hazard of DM experience 3.23 times in total participants [HR (CI 95%): 3.23 (2.57, 4.07); p鈥�<鈥�0.001], 2.35 times in menopausal participants [HR (CI 95%): 2.35 (1.71, 3.24); p-value鈥�<鈥�0.001] and 6.53 times in reproductive age participants [HR (CI 95%): 6.53 (3.98, 10.73); p鈥�<鈥�0.001] significantly in unadjusted models. After adjusting for potential confounders, hazard of DM was shown more attenuated than before.

The results of adjusted models showed that in total participants the high BRI trajectory compared to the low trajectory in total subjects intensified the hazard of DM experience 2.48 times [HR (CI 95%): 2.48 (1.92, 3.21); p鈥�<鈥�0.001], 2.20 times in menopausal women [HR (CI 95%): 2.20 (1.55, 3.11); p鈥�<鈥�0.001] and 4.90 times in reproductive age women [HR (CI 95%): 4.90 (2.85, 8.42); p鈥�<鈥�0.001], respectively.

Cox proportional hazard models in unadjusted model shows total sample in the high trajectory of C-index had 73% higher hazard of developing DM compared to the low trajectory of C-index [HR (CI 95%): 1.73 (1.49, 2.88); p鈥�&濒迟;鈥�0.001闭. However, after adjustment, this hazard significantly increased to 2.24 times [HR (CI 95%): 2.24 (1.69, 2.97); p-value鈥�&濒迟;鈥�0.001闭. While in menopausal age, women in high C-index trajectory compared to the low trajectory were 2.07 times [HR (CI 95%): 2.07 (1.49, 2.88); p鈥�<鈥�0.001] in unadjusted model and 1.80 times [HR (CI 95%):1.80 (1.26, 2.58); p-value鈥�=鈥�0.001] more likely to experience DM. In reproductive age participants, high BRI trajectory compared to the low trajectory intensified the hazard of DM to 5.12 times [HR (CI 95%):5.12 (3.12, 8.42), p鈥�<鈥�0.001] in unadjusted model. The hazard decreased after adjustment to 4.32 times [HR (CI 95%): 4.32 (2.49, 7.47); p鈥�&濒迟;鈥�0.001闭.

Reproductive age women in high class of WC were 4.71 times [HR (CI 95%):4.71 (2.86, 7.75)]; p鈥�<鈥�0.001] before adjustment and 3.62 times [HR (CI 95%):3.62 (2.07, 6.30); p-value鈥�<鈥�0.001] after adjustment at higher hazard of DM compared to those in low class.

In total subjects, women in high class of WC were 3.06 times and 2.30 times more likely to experience DM compared to those in low trajectory of WC in unadjusted and adjusted models, respectively [HR (CI 95%):3.06 (2.41, 3.89), p鈥�<鈥�0.001 and HR (CI 95%): 2.30 (1.78, 2.98); p鈥�&濒迟;鈥�0.001)闭.

In menopausal women, this hazard was 2.55 times [HR (CI 95%): 2.55 (1.80, 3.60); p鈥�<鈥�0.001] higher in unadjusted model and 2.29 times [[HR (CI 95%): 2.29 (1.58, 3.30); p-value鈥�<鈥�0.001] higher in adjusted model. In reproductive- age women, this hazard obtained 4.71 times more in the unadjusted model [HR (CI 95%): 4.71 (2.86, 7.75); p鈥�<鈥�0.001] and 3.62 times more in adjusted model [HR (CI 95%): 3.62 (2.07, 6.30); p鈥�&濒迟;鈥�0.001闭.

Discussion

In this study, we identified three distinct trajectories for BMI, and two trajectories for each of the other adiposity indices (C-index, WC, and BRI) that were associated with incidence of DM. Among total subjects, and in both subgroups of menopause and reproductive age women鈥檚 risk of DM was greater for the high class of BMI, WC, C-index, and BRI trajectories than the lower trajectories. All adiposity indices, including general (BMI), abdominal (C-index, WC), and visceral (BRI) are potent risk factors for predicting DM incidence.

The exact mechanism that identifies the link between DM and adiposity indices is not clear. Insulin resistance (IR) is known as a common factor in the pathogenesis of obesity, in which factors like endoplasmic reticulum stress, adipose tissue hypoxia, oxidative stress, lipodystrophy, and genetic background have a role in insulin resistance contributing to the pathway of developing IR [47]. Evidence also points to the key role of females鈥� gluteal-femoral adipose tissue distribution in protecting against metabolic disturbances in women rather than men [48]. The association between gynoid adiposity and cardio-metabolic risk factors is not as strong [49].

There are several cohort studies on the association of DM and obesity using the conventional approaches [50,51,52]. Actual patterns of anthropometric variables might be affected by various methods of analysis which are applied in different studies including hierarchical clustering, the latent class growth mixed model, and semiparametric mixture models [53,54,55]. We did not employ predefined cut-offs to categorize the various groups of adiposity indices. Instead, we utilized trajectory modeling to account for all variations and to identify individuals with similar progressions over time. This approach enables us to determine individuals with comparable patterns of risk factors, thereby enhancing risk stratification. These distinct trajectories emerged naturally from the continuous data during the modeling process, preserving the full range of variability in adiposity index measurements.

Meanwhile, our study showed that as adiposity worsened, the risk of DM became greater when compared with subjects with lower BMI, C-index, WC, and BRI. Among the studies looking into the association between the underlying trajectories of adiposity indices and risk of DM, Peter et al. in their study among Austrian adults by using growth mixture modeling identified four trajectory classes of BMI for the age groups鈥�<鈥�50 years and 50 to 65 years, and three trajectory classes for age groups鈥�>鈥�65 years [56]. The Rotterdam Study, which used a linear mixed-effects model, identified three distinct trajectories of BMI, but WC is also undergoing a similar downward trend as BMI [57]. A systematic review of 14 population-based cohort studies, found that the stable, increasing, decreasing, and turning groups were the most common BMI trajectories. They also reported that the decreasing trend of BMI from adolescence to adulthood could lead to dropping the incidence of DM in later life [58]. The English Longitudinal Study of Aging by using GMM identified four latent BMI trajectories (stable overweight, elevated BMI, increasing BMI, and decreasing BMI) associated with DM, in this study only adults over 50 years of age were included and using a two-step approach plus to growth鈥恗ixture model and discrete鈥恡ime survival analysis were applied for this study [59]. The various findings of studies might be related to the age, ethnicity, lifestyle, and anthropometric indices of participants selected for analysis.

Our finding showed that the association of central and visceral adiposity indexes with DM is stronger than the association of general adiposity indexes and DM. There is controversy regarding whether general or central obesity is better for predicting DM [60,61,62]. Visceral obesity rather than other types of obesity can contribute to the pathogenesis of impaired glucose metabolism through different pathways [63]. Furthermore, using a single BMI to assess obesity can ignore the higher body fat, especially among people with a normal BMI [64]. However, a review supported that higher body fat levels lead to better predictions of DM [65].

We found that a high class of adiposity indicators was associated with an increased risk of DM in both groups but with a stronger association in premenopausal women than in postmenopausal women. Prior studies have also demonstrated that younger women are more likely to develop DM compared to the older [66]. Along with our findings, the study of Lee et al. (2021) demonstrated that the premenopausal women for the link between obesity and DM were stronger than postmenopausal women [67]. A recent study among subjects age groups 30 to 59 and 60 to 74 years revealed that after 3 years of follow-up, the risk of development DM related to central adiposity was higher for people of younger age groups than for older [68]. The risk of mortality per 5听kg/m2 unit of higher BMI in the young population is higher than that of older individuals, as shown by a meta-analysis [69]. Against premenopausal obese and overweight women, postmenopausal women might have higher estradiol levels, which can attenuate the association between obesity and developing DM due to the protective role of estrogen [70, 71]. In menopausal women, aromatization of androgens results in estrogens senthesiz in adipose tissue and obese postmenopausal women are protected against DM [22]. Moreover, menopause transition is associated with loss of lean mass and gain fat mass and fat mass redistributes from gynoid to abdominal obesity [72, 73]. Although, during reproductive periods lean mass is higher than during menopause, high-fat mass and high lean mass may not protect against DM risk [74].

Hormonal changes are the main driving factor in the development of diabetes. According to the study of Ding et al. (2007), sex steroid hormone levels are strongly associated with the risk of DM in women [75]. Since menopause coincides with aging, it is quite difficult to evaluate the exact impact of menopause on DM risk.

The main strength of this study is its prospective design which allowed for repeated measurements of adiposity indices and the use of latent class trajectory analysis. Additionally, the large sample size with a single Asian ethnicity is strength. Standard protocol measurements for both exposure and outcome variables were used, reducing the potential for self-reported bias. Self-reported measurements might lead to heterogeneous patterns of different anthropometric variables. Furthermore, the study included a variety of adiposity indices rather than solely relying on BMI. These findings are particularly noteworthy, as we adjusted for several key socio-demographic, behavioral, and biological covariates. Moreover, we have stratified women according to the menopause status, which gives the ability to translate our findings across the lifespan of women. However, limitations should also be considered. The study was limited to an urban population, potentially limiting the generalizability of the findings. Unmeasured confounders may have influenced the results despite adjusting for measured variables. Additionally, the rate of loss to follow-up may have impacted the results. However, in this study, we were unable to evaluate gynoid obesity by Dexa body composition analysis. The number of underlying trajectories that best describe the data can be affected by the time between measurement points and follow-up time [76]. In our study, the sample size and follow-up time might affect the number of classes.

Conclusion

These findings could help provide new insight into the prevention of Iranian women鈥檚 DM risk. The risk of DM was greater for the high BMI, WC, C-index, and BRI trajectories than for the lower trajectories. Hence, the development of general, abdominal, and visceral obesity trajectories in the prevention of DM should be considered by clinicians.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

DM:

Diabetes Mellitus

TLGS:

Tehran lipid and glucose

BMI:

Body mass index

CI:

Confidence intervals

HR:

Hazard ratio

FBS:

Fasting blood sugar

C-index:

Conicity Index

BRI:

Body Roundness Index

WC:

Waist circumference

WHR:

Waist to hip ratio

WHtR:

Waist to height ratio

MetS:

Metabolic syndrome

MET:

Metabolic equivalent

VAT:

Visceral adipose tissue

AVI:

Abdominal volume index

IR:

Insulin resistance

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Acknowledgements

We thank the participating and research assistants from the TLGS research center who took part in this study.

Funding

This study funded by the Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran (Grant number: 8-43007429).

Author information

Authors and Affiliations

  1. Reproductive Endocrinology Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Maryam Mousavi,听Marzieh Saei Ghare Naz听&听Fahimeh Ramezani Tehrani

  2. Tehran Medical Branch, Islamic Azad University, Tehran, Iran

    Faegheh Firouzi

  3. Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Fereidoun Azizi

  4. The foundation for research & Education Excellence, Vestaria Hills, Al, USA

    Fahimeh Ramezani Tehrani

Authors
  1. Maryam Mousavi

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  2. Marzieh Saei Ghare Naz

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  3. Faegheh Firouzi

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  4. Fereidoun Azizi

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  5. Fahimeh Ramezani Tehrani

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Contributions

Conception or design: M.S.G.,F.R.T.,M.M. Acquisition, analysis, or interpretation of data: M.S.G.,M.M. Drafting the work or revising: F.R.T., M.S.G.,F.F.,F.A.,M.M. Final approval of the manuscript: F.R.T., M.S.G.,F.F.,F.A.,M.M.

Corresponding author

Correspondence to Marzieh Saei Ghare Naz.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the ethics committee of the Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences. Informed consent was obtained in accordance with the Declaration of Helsinki.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Mousavi, M., Saei Ghare Naz, M., Firouzi, F. et al. Impact of adiposity indices changes across the lifespan on risk of diabetes in women: trajectory modeling approach. 樱花视频 24, 2429 (2024). https://doi.org/10.1186/s12889-024-19996-4

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  • DOI: https://doi.org/10.1186/s12889-024-19996-4

Keywords

樱花视频

ISSN: 1471-2458

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