In this study, we have identified two clusters by factor analysis including body fat cluster consisting of VFA, BMI, WC, PBF, and nutritional parameters cluster consisting of PA50, hemoglobin, BCM, and albumin. After dividing participants into the four groups based on the cluster scores, we have demonstrated that those with poor nutritional parameters were more likely to have decreased eGFR and increased UACR, whereas participants with low body fat and good nutritional parameters were less commonly to have DKD. Our noteworthy finding is that particular high body fat and poor nutritional parameters were significantly associated with increased odds of having DKD in this study population. We therefore suggest that body fat and nutritional parameters might be promising markers representing metabolic state and signify their importance in the pathogenesis of DKD. Clinical utility of BIA might provide valuable recommendations to patients with T2DM.

DKD risk factors are multifactorial and complex, involving genetic and environmental factors, which mainly derived from cohort studies with different sample sizes [5, 6]. Results of these studies were not completely consistent, and the only proven primary prevention interventions for DKD are merely blood glucose and blood pressure control [12]. For the first time to our knowledge, this study has investigated the relation of a composite indicator of body fat and nutritional parameters to the presence of DKD in patients with T2DM. Nutritional assessment includes several categories: BMI, body composition (e.g., PBF, PA50), blood biomarkers (e.g., albumin, hemoglobin), nutritional screening tool (Mini Nutritional Assessment-Short Form), anthropometric measurements (e.g., weight, WC), and dietary assessment (e.g., food and energy intake) [17]. Optimal, universal, and reliable nutritional status screening questionnaires and equations are still lacking. Body composition measure and biochemical test are precise and reliable as well as inexpensive and simple [18]. Interestingly, dimensionality reduction clustering by factor analysis distinguished two clusters, which are integrated as cluster of body fat (VFA, BMI, WC, and PBF) and cluster of nutritional parameters (PA50, hemoglobin, BCM, and albumin). A previous cross-sectional study performed in subjects with T2DM and age- and BMI-matched control subjects indicated that phase angle was a promising measurement for assessing catabolic state in people with diabetes [19]. Phase angle reflecting the poor nutritional status was associated with the lean tissue index, hemoglobin level, albumin level, and eGFR in patients with diabetic CKD stage 5 [20]. The association between scores of body fat cluster or nutritional parameters cluster and characteristics of the T2DM suggested several confounding factors, such as hypertension, gender, age, smoking, duration of diabetes, and stages of DKD. However, the association of body fat and nutrition with DKD was still reliable after adjustment of several possible confounding factors and coexisting comorbidities.

Findings from this study demonstrating that high body fat and poor nutritional parameters are strongly associated with the presence of DKD are pathophysiologically plausible. Diet insecurity may increase the risk and progression of diabetes complications through nutritional pathways and promising food security interventions have demonstrated positive impacts on diabetes outcomes [21]. The significance of the relationship between adipose tissue and T2DM has long been cemented. Distribution of adipose tissue rather than the total amount is more crucial in the development of vascular complications in Asian patients with T2DM [22]. Perirenal fat thickness significantly raised the risk for CKD in patients with diabetes [10]. Visceral adiposity and abdominal obesity are more closely associated with DKD [23, 24]. However, our knowledge of the multifaceted role of fat in disease progression is evolving and expanding [25, 26]. First, fat or adipose tissue has been far beyond a simple depot for energy storage. Reports have highlighted their endocrine signaling in insulin resistance [27]. A multitude of bioactive compounds, including adipokines, cytokines, and other lipid biomolecules, are actively secreted by adipose tissue, which have potential impacts on metabolism [28]. Second, adipose tissue comprises a multitude of different cell types besides adipocytes and preadipocytes, such as fibroblasts, endothelial cells, and immune cells, which have distinct contributions [29]. Third, different compartments of fat, including subcutaneous adipose depot, visceral adipose depot, and specifically adipose in and around metabolic organs, such as pancreas, skeletal muscle, vasculature, and kidney, have received most attention with regard to metabolic diseases [30]. Moreover, the significant distinctions between brown, beige, white, and pink adipocytes and the plasticity of adipose tissues impact the pathogenesis of various diseases [31]. We still do not have comprehensive understandings of the connections between fat and T2DM. There are even more large holes in our knowledge of fat and DKD.

This study has important clinical implications. To date, several medications have been applied to attenuate the progression of DKD [32]. However, blockade of renin–angiotensin system was not associated with attenuation of long-term risk of GFR decline [33], especially among patients with advanced CKD (eGFR < 30 ml/min/1.73 m2) [34]. Sodium–glucose cotransporter 2 (SGLT2) inhibitors prevented progression of CKD in patients with diabetes [35, 36], and evidence suggested that glucagon-like peptide 1 receptor agonists (GLP-1RA) had benefits for the kidney [37]. Mineralocorticoid receptor antagonists (MRA) have proven to be effective in reducing kidney disease progression in patients with diabetes [38]. However, the aforementioned actions of SGLT2 inhibitors, GLP-1RA, and MRA were largely consistent with improved glucose and BP control, as well as significant weight loss and improving inflammation and dysmetabolism. Medications specifically to prevent and treat DKD are quite limited; management strategies usually include lifestyle modifications, including diet interventions, physical activities, and weight control. Studies have evaluated the role of nutrition interventions in the management of T2DM and the positive results are predictable. A clinically recommended low-protein diet is expected to retard renal function decline in DKD [39]. The diet prescription may include nutrient types (e.g., carbohydrate, fat, micronutrients, vitamins), energy, and glycemic index, which should be tailored to meet the needs and characteristics of each patient [40].

We acknowledge several limitations in this study. First, the cross-sectional design used here limited the time inference of the predictor and outcomes. Prospective studies are needed to evaluate the sequence of these associations.

Second, the diagnosis of DKD was based on UACR and eGFR, which is less accurate than kidney pathology. However, renal biopsy is still at risk of missing atypical DKD, and its indication in patients with diabetes is controversial [41, 42]. The typical presentation of DKD is considered to include a long-standing duration of diabetes, retinopathy, albuminuria without gross hematuria, and gradually progressive loss of eGFR. However, signs of DKD may be present at diagnosis [2, 43, 44] or without retinopathy in T2DM [45]. Reduced eGFR without albuminuria has been frequently reported in T2DM and is becoming more common [46]. We could have difficulties in differentiating patients with DKD and without albuminuria or reduced eGFR from patients with NDKD. The associations might have been stronger if more rigorous diagnostic methods were adopted.

Third, we applied BIA instead of standard methods such as computed tomography (CT) or dual-energy X-ray absorptiometry (DXA) [47] to measure body composition in our study. CT has limitations such as radiation risk, high cost, and not being suitable for ubiquitous and frequent use. DXA is expensive and requires specialized radiology equipment and thus is hardly feasible in routine clinical practice. However, BIA is commonly available and used in clinical practice and research studies to evaluate body composition including VFA. This method measures electrical data including resistance, reactance, and impedance of the body that are less affected by factors such as daily diet or exercise [48]. Moreover, BIA has good reliability, accuracy, and clinical feasibility when compared with standard methods in healthy and obesity people, as well as subjects with T2DM [7, 49,50,51,52]. In addition, the correlation of daily dietary intake of nutrients and exercise with the nutrition parameters estimated by BIA might be interesting, and more rigorous research is needed to understand how to design and implement these programs for populations with diabetes.